DataTalks.Club: Recent Episodes

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hi everyone Welcome to our event this event is brought to you by data dos club which is a community of people who love

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data and we have weekly events and today one is one of such events and I guess we

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are also a community of people who like to wake up early if you're from the states right Christopher or maybe not so

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much because this is the time we usually have uh uh our events uh for our guests

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and presenters from the states we usually do it in the evening of Berlin time but yes unfortunately it kind of

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slipped my mind but anyways we have a lot of events you can check them in the

0:41

description like there's a link um I don't think there are a lot of them right now on that link but we will be

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adding more and more I think we have like five or six uh interviews scheduled so um keep an eye on that do not forget

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to subscribe to our YouTube channel this way you will get notified about all our future streams that will be as awesome

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as the one today and of course very important do not forget to join our community where you can hang out with

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other data enthusiasts during today's interview you can ask any question there's a pin Link in live chat so click

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on that link ask your question and we will be covering these questions during the interview now I will stop sharing my

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screen and uh there is there's a a message in uh and Christopher is from

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you so we actually have this on YouTube but so they have not seen what you wrote

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but there is a message from to anyone who's watching this right now from Christopher saying hello everyone can I

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call you Chris or you okay I should go I should uh I should look on YouTube then okay yeah but anyways I'll you don't

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need like you we'll need to focus on answering questions and I'll keep an eye

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I'll be keeping an eye on all the question questions so um

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yeah if you're ready we can start I'm ready yeah and you prefer Christopher

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not Chris right Chris is fine Chris is fine it's a bit shorter um

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okay so this week we'll talk about data Ops again maybe it's a tradition that we talk about data Ops every like once per

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year but we actually skipped one year so because we did not have we haven't had

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Chris for some time so today we have a very special guest Christopher Christopher is the co-founder CEO and

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head chef or hat cook at data kitchen with 25 years of experience maybe this

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is outdated uh cuz probably now you have more and maybe you stopped counting I

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don't know but like with tons of years of experience in analytics and software engineering Christopher is known as the

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co-author of the data Ops cookbook and data Ops Manifesto and it's not the

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first time we have Christopher here on the podcast we interviewed him two years ago also about data Ops and this one

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will be about data hops so we'll catch up and see what actually changed in in

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these two years and yeah so welcome to the interview well thank you for having

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me I'm I'm happy to be here and talking all things related to data Ops and why

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why why bother with data Ops and happy to talk about the company or or what's changed

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excited yeah so let's dive in so the questions for today's interview are prepared by Johanna berer as always

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thanks Johanna for your help so before we start with our main topic for today

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data Ops uh let's start with your ground can you tell us about your career Journey so far and also for those who

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have not heard have not listened to the previous podcast maybe you can um talk

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about yourself and also for those who did listen to the previous you can also maybe give a summary of what has changed

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in the last two years so we'll do yeah so um my name is Chris so I guess I'm

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a sort of an engineer so I spent about the first 15 years of my career in

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software sort of working and building some AI systems some non- AI systems uh

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at uh Us's NASA and MIT linol lab and then some startups and then um

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Microsoft and then about 2005 I got I got the data bug uh I think you know my

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kids were small and I thought oh this data thing was easy and I'd be able to go home uh for dinner at 5 and life

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would be fine um because I was a big you started your own company right and uh it didn't work out that way

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and um and what was interesting is is for me it the problem wasn't doing the

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data like I we had smart people who did data science and data engineering the act of creating things it was like the

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systems around the data that were hard um things it was really hard to not have

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errors in production and I would sort of driving to work and I had a Blackberry at the time and I would not look at my

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Blackberry all all morning I had this long drive to work and I'd sit in the parking lot and take a deep breath and

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look at my Blackberry and go uh oh is there going to be any problems today and I'd be and if there wasn't I'd walk and

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very happy um and if there was I'd have to like rce myself um and you know and

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then the second problem is the team I worked for we just couldn't go fast enough the customers were super

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demanding they didn't care they all they always thought things should be faster and we are always behind and so um how

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do you you know how do you live in that world where things are breaking left and right you're terrified of making errors

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um and then second you just can't go fast enough um and it's preh Hadoop era

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right it's like before all this big data Tech yeah before this was we were using

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uh SQL Server um and we actually you know we had smart people so we we we

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built an engine in SQL Server that made SQL Server a column or

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database so we built a column or database inside of SQL Server um so uh

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in order to make certain things fast and and uh yeah it was it was really uh it's not

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bad I mean the principles are the same right before Hadoop it's it's still a database there's still indexes there's

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still queries um things like that we we uh at the time uh you would use olap

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engines we didn't use those but you those reports you know are for models it's it's not that different um you know

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we had a rack of servers instead of the cloud um so yeah and I think so what what I

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took from that was uh it's just hard to run a team of people to do do data and analytics and it's not

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really I I took it from a manager perspective I started to read Deming and

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think about the work that we do as a factory you know and in a factory that produces insight and not automobiles um

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and so how do you run that factory so it produces things that are good of good

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quality and then second since I had come from software I've been very influenced

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by by the devops movement how you automate deployment how you run in an agile way how you

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produce um how you how you change things quickly and how you innovate and so

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those two things of like running you know running a really good solid production line that has very low errors

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um and then second changing that production line at at very very often they're kind of opposite right um and so

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how do you how do you as a manager how do you technically approach that and

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then um 10 years ago when we started data kitchen um we've always been a profitable company and so we started off

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uh with some customers we started building some software and realized that we couldn't work any other way and that

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the way we work wasn't understood by a lot of people so we had to write a book and a Manifesto to kind of share our our

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methods and then so yeah we've been in so we've been in business now about a little over 10

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years oh that's cool and uh like what

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uh so let's talk about dat offs and you mentioned devops and how you were inspired by that and by the way like do

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you remember roughly when devops as I think started to appear like when did people start calling these principles

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and like tools around them as de yeah so agile Manifesto well first of all the I

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mean I had a boss in 1990 at Nasa who had this idea build a

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little test a little learn a lot right that was his Mantra and then which made

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made a lot of sense um and so and then the sort of agile software Manifesto

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came out which is very similar in 2001 and then um the sort of first real

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devops was a guy at Twitter started to do automat automated deployment you know

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push a button and that was like 200 Nish and so the first I think devops

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Meetup was around then so it's it's it's been 15 years I guess 6 like I was

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trying to so I started my career in 2010 so I my first job was a Java

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developer and like I remember for some things like we would just uh SFTP to the

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machine and then put the jar archive there and then like keep our fingers crossed that it doesn't break uh uh like

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it was not really the I wouldn't call it this way right you were deploying you

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had a Dey process I put it yeah

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right was that so that was documented too it was like put the jar on production cross your

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fingers I think there was uh like a page on uh some internal Viki uh yeah that

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describes like with passwords and don't like what you should do yeah that was and and I think what's interesting is

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why that changed right and and we laugh at it now but that was why didn't you

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invest in automating deployment or a whole bunch of automated regression

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tests right that would run because I think in software now that would be rare

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that people wouldn't use C CD they wouldn't have some automated tests you know functional

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regression tests that would be the exception whereas that the norm at the beginning of your career and so that's

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what's interesting and I think you know if we if we talk about what's changed in the last two three years I I think it is

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getting more standard there are um there's a lot more companies who are

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talking data Ops or data observability um there's a lot more tools that are a lot more people are

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using get in data and analytics than ever before I think thanks to DBT um and

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there's a lot of tools that are I think getting more code Centric right that

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they're not treating their configuration like a black box there there's several

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bi tools that tout the fact that they that they're uh you know they're they're git Centric you know and and so and that

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they're testable and that they have apis so things like that I think people maybe let's take a step back and just do a

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quick summary of what data Ops data Ops is and then we can talk about like what changed in the last two years sure so I

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guess it starts with a problem and that it's it sort of

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admits some dark things about data and analytics and that we're not really successful and we're not really happy um

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and if you look at the statistics on sort of projects and problems and even

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the psychology like I think about a year or two we did a survey of

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data Engineers 700 data engineers and 78% of them wanted their job to come with a therapist and 50% were thinking

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of leaving the career altogether and so why why is everyone sort of unhappy well I I I think what happens is

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teams either fall into two buckets they're sort of heroic teams who

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are doing their they're working night and day they're trying really hard for their customer um and then they get

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burnt out and then they quit honestly and then the second team have wrapped

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their projects up in so much process and proceduralism and steps that doing

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anything is sort of so slow and boring that they again leave in frustration um

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or or live in cynicism and and that like the only outcome is quit and

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start uh woodworking yeah the only outcome really is quit and start working

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and um as a as a manager I always hated that right because when when your team

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is either full of heroes or proceduralism you always have people who have the whole system in their head

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they're certainly key people and then when they leave they take all that knowledge with them and then that

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creates a bottleneck and so both of which are aren aren't and I think the

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main idea of data Ops is there's a balance between fear and herois

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that you can live you don't you know you don't have to be fearful 95% of the time maybe one or two% it's good to be

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fearful and you don't have to be a hero again maybe one or two per it's good to be a hero but there's a balance um and

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and in that balance you actually are much more prod

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In this podcast episode, we talked with Guillaume Lemaître about navigating scikit-learn and imbalanced-learn.🔗 CONNECT WITH Guillaume LemaîtreLinkedIn - https://www.linkedin.com/in/guillaume-lemaitre-b9404939/ Twitter - https://x.com/glemaitre58Github - https://github.com/glemaitreWebsite - https://glemaitre.github.io/🔗 CONNECT WITH DataTalksClubJoin the community - https://datatalks-club.slack.com/join/shared_invite/zt-2hu0sjeic-ESN7uHt~aVWc8tD3PefSlA#/shared-invite/email Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/u/0/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQCheck other upcoming events - https://lu.ma/dtc-events LinkedIn - https://www.linkedin.com/company/datatalks-club/ Twitter - https://twitter.com/DataTalksClub Website - https://datatalks.club/ 🔗 CONNECT WITH ALEXEYTwitter - https://twitter.com/Al_Grigor Linkedin - https://www.linkedin.com/in/agrigorev/ 🎙 ABOUT THE PODCASTAt DataTalksClub, we organize live podcasts that feature a diverse range of guests from the data field. Each podcast is a free-form conversation guided by a prepared set of questions, designed to learn about the guests’ career trajectories, life experiences, and practical advice. These insightful discussions draw on the expertise of data practitioners from various backgrounds.We stream the podcasts on YouTube, where each session is also recorded and published on our channel, complete with timestamps, a transcript, and important links.You can access all the podcast episodes here - https://datatalks.club/podcast.html📚Check our free online coursesML Engineering course - http://mlzoomcamp.comData Engineering course - https://github.com/DataTalksClub/data-engineering-zoomcamp MLOps course - https://github.com/DataTalksClub/mlops-zoomcamp Analytics in Stock Markets - https://github.com/DataTalksClub/stock-markets-analytics-zoomcamp LLM course - https://github.com/DataTalksClub/llm-zoomcamp Read about all our courses in one place - https://datatalks.club/blog/guide-to-free-online-courses-at-datatalks-club.html 👋🏼 GET IN TOUCH If you want to support our community, use this link - https://github.com/sponsors/alexeygrigorev If you're a company and want to support us, contact at alexey@datatalks.club

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Links:

• LinkedIn:https://www.linkedin.com/company/frontline100/
• Ba Linh Le's LinkedIn: https://www.linkedin.com/in/ba-linh-le-/
• Sabrina's LinkedIn: https://www.linkedin.com/in/sabina-firtala/
• Twitter: https://x.com/frontline_100?mx=2
• Website: https://www.frontline100.com/

Free LLM course: https://github.com/DataTalksClub/llm-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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We stream the podcasts on YouTube, where each session is also recorded and published on our channel, complete with timestamps, a transcript, and important links.You can access all the podcast episodes here - https://datatalks.club/podcast.html📚Check our free online coursesML Engineering course - http://mlzoomcamp.comData Engineering course - https://github.com/DataTalksClub/data-engineering-zoomcamp MLOps course - https://github.com/DataTalksClub/mlops-zoomcamp Analytics in Stock Markets - https://github.com/DataTalksClub/stock-markets-analytics-zoomcamp LLM course - https://github.com/DataTalksClub/llm-zoomcamp Read about all our courses in one place - https://datatalks.club/blog/guide-to-free-online-courses-at-datatalks-club.html 👋🏼 GET IN TOUCH If you want to support our community, use this link - https://github.com/sponsors/alexeygrigorev If you’re a company, support us at alexey@datatalks.club

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Links:

• LinkedIn: https://www.linkedin.com/in/erum-afzal-64827b24/
• Twitter: https://twitter.com/Erum55449739

Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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Links:

• probabl. YouTube channel: https://www.youtube.com/@UCIat2Cdg661wF5DQDWTQAmg
• Calmcode website: https://calmcode.io/
• probabl. website: https://probabl.ai/

Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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Links:

• Biodiversity and Artificial Intelligence pdf: https://www.gpai.ai/projects/responsible-ai/environment/biodiversity-and-AI-opportunities-recommendations-for-action.pdf

Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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Links:

• GitHub repo: https://github.com/antahiap/ADPT-LRN-PHYS/tree/main

Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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Links:

• LinkedIn: https://www.linkedin.com/in/tereza-iofciu/
• Twitter: https://twitter.com/terezaif
• Github: https://github.com/terezaif
• Website: https:// terezaiofciu.com

Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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Links:

• VectorHub: https://superlinked.com/vectorhub/?utm_source=community&utm_medium=podcast&utm_campaign=datatalks
• Daniel's LinkedIn: https://www.linkedin.com/in/svonava/

Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html​This podcast is sponsored by VectorHub, a free open-source learning community for all things vector embeddings and information retrieval systems.

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We talked about:

• Reem’s background
• Context-aware sensing and transfer learning
• Shifting focus from PhD to industry
• Reem’s experience with startups and dealing with prejudices towards PhDs
• AI interviewing solution
• How candidates react to getting interviewed by an AI avatar
• End-to-end overview of a machine learning project
• The pitfalls of using LLMs in your process
• Mitigating biases
• Addressing specific requirements for specific roles
• Reem’s resource recommendations

Links:

• LinkedIn: https://www.linkedin.com/in/reemmahmoud/recent-activity/all/
• Website: https://topmate.io/reem_mahmoud

Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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We talked about:

• Sara’s background
• On being a Google PhD fellow
• Sara’s volunteer work
• Finding AI volunteer work
• Sara’s Fruit Punch challenge
• How to take part in AI challenges
• AI Wonder Girls
• Hackathons
• Things people often miss in AI projects and hackathons
• Getting creative
• Fostering your social media
• Tips on applying for volunteer projects
• Why it’s worth doing volunteer projects
• Opportunities for data engineers and students
• Sara’s newsletter suggestions

Links:

• Dev and AI hackathons: https://devpost.com/
• Healthcare-focused challenges: https://grand-challenge.org/challenges/
• Volunteering in projects (AI4Good): https://www.fruitpunch.ai/
• Volunteering in projects (AI4Good) 2: https://www.omdena.com/
• Twitter: https://twitter.com/el_ateifSara
• Instagram: https://www.instagram.com/saraelateif/
• LinkedIn: https://www.linkedin.com/in/sara-el-ateif/
• Youtube: www.youtube.com/@elateifsara

Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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We talked about:

• Sarah’s background
• How Sarah became a coach and found her niche
• Sarah’s clients
• How Sarah helps her clients find the perfect job
• Finding a specialization
• Informational interviews
• Building a connection for mutual benefit
• The networking strategy
• Listing your projects in the CV
• The importance of doing research yourself and establishing your interests
• How to land a part-time job when the company wants full-time
• Age is not a factor
• Applying for jobs after finishing a course and the importance of sharing your learnings
• Sarah resource recommendations

Links:

• LinkedIn: https://www.linkedin.com/in/sarahmestiri/
• Website: https://thrivingcareermoms.com/
• Personal Website: https://www.sarahmestiri.com/
• Youtube channel: https://www.youtube.com/@thrivingcareermoms444

Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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We talked about:

• Nemanja’s background
• When Nemanja first work as a data person
• Typical problems that ML Ops folks solve in the financial sector
• What Nemanja currently does as an ML Engineer
• The obstacle of implementing new things in financial sector companies
• Going through the hurdles of DevOps
• Working with an on-premises cluster
• “ML Ops on a Shoestring” (You don’t need fancy stuff to start w/ ML Ops)
• Tactical solutions
• Platform work and code work
• Programming and soft skills needed to be an ML Engineer
• The challenges of transitioning from and electrical engineering and sales to ML Ops
• The ML Ops tech stack for beginners
• Working on projects to determine which skills you need

Links:

• LinkedIn: https://www.linkedin.com/in/radojkovic/

Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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We talked about:

• Ivan’s background
• How Ivan became interested in investing
• Getting financial data to run simulations
• Open, High, Low, Close, Volume
• Risk management strategy
• Testing your trading strategies
• Sticking to your strategy
• Important metrics and remembering about trading fees
• Important features
• Deployment
• How DataTalks.Club courses helped Ivan
• Ivan’s site and course sign-up

Links:

• Exploring Finance APIs: https://pythoninvest.com/long-read/exploring-finance-apis
• Python Invest Blog Articles: https://pythoninvest.com/blog

Free ML Engineering course: http://mlzoomcamp.comJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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We talked about:

• Rob’s background
• Going from software engineering to Bayesian modeling
• Frequentist vs Bayesian modeling approach
• About integrals
• Probabilistic programming and samplers
• MCMC and Hakaru
• Language vs library
• Encoding dependencies and relationships into a model
• Stan, HMC (Hamiltonian Monte Carlo) , and NUTS
• Sources for learning about Bayesian modeling
• Reaching out to Rob

Links:

• Book 1: https://bayesiancomputationbook.com/welcome.html
• Book/Course: https://xcelab.net/rm/statistical-rethinking/

Free ML Engineering course: http://mlzoomcamp.comJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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We talked about:

• Atita’s background
• How NLP relates to search
• Atita’s experience with Lucidworks and OpenSource Connections
• Atita’s experience with Qdrant and vector databases
• Utilizing vector search
• Major changes to search Atita has noticed throughout her career
• RAG (Retrieval-Augmented Generation)
• Building a chatbot out of transcripts with LLMs
• Ingesting the data and evaluating the results
• Keeping humans in the loop
• Application of vector databases for machine learning
• Collaborative filtering
• Atita’s resource recommendations

Links:

• LinkedIn: https://www.linkedin.com/in/atitaarora/
• Twitter: https://x.com/atitaarora
• Github: https://github.com/atarora
• Human-in-the-Loop Machine Learning: https://www.manning.com/books/human-in-the-loop-machine-learning
• Relevant Search: https://www.manning.com/books/relevant-search
• Let's learn about Vectors: https://hub.superlinked.com/Langchain: https://python.langchain.com/docs/get_started/introduction
• Qdrant blog: https://blog.qdrant.tech/
• OpenSource Connections Blog: https://opensourceconnections.com/blog/

Free ML Engineering course: http://mlzoomcamp.comJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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We talked about:

• Adrian’s background
• The benefits of freelancing
• Having an agency vs freelancing
• What let Adrian switch over from freelancing
• The conception of DLT (Growth Full Stack)
• The investment required to start a company
• Growth through the provision of services
• Growth through teaching (product-market fit)
• Moving on to creating docs
• Adrian’s current role
• Strategic partnerships and community growth through DocDB
• Plans for the future of DLT
• DLT vs Airbyte vs Fivetran
• Adrian’s resource recommendations

Links:

• Adrian's LinkedIn: https://www.linkedin.com/in/data-team/
• Twitter: https://twitter.com/dlt_library
• Github: https://github.com/dlt-hub/dlt
• Website: https://dlthub.com/docs/intro

Free ML Engineering course: http://mlzoomcamp.comJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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We talked about:

• Dimitri’s background
• The first steps of transitioning into freelance
• Working with recruiters (contracting)
• Deciding on what to charge for your services
• Establishing your network
• Self-marketing
• Contracting vs freelancing
• Which channel is better for those starting out?
• Cutting out the middleman
• Where to look for clients and how to vet them
• The different way of getting into freelancing
• Going back to a full-time job after freelancing
• Common mistakes freelancers make
• Dimitri’s resource suggestions
• Reaching out to Dimitri

Links:

• LinkedIn profile: http://www.linkedin.com/in/visnadi
• The DataFreelancer website: https://thedatafreelancer.com/

Free ML Engineering course: http://mlzoomcamp.comJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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We talked about:

• Maria’s background
• Deciding to go into telecare (healthcare)
• Current difficulties in healthcare
• Getting into the healthcare industry as a lifestyle brand
• The importance of a plan B and being flexible
• What is SQIN and the importance of communication
• Going from lipstick to skin health analysis
• The importance of community and broadening your audience
• The importance of feedback and communicating benefits
• The current state and growth of SQIN
• Convincing investors and the importance of proving profitability
• Maria’s role at SQIN
• Balancing a newborn child and a new company

Links:

• Free ML Engineering course: http://mlzoomcamp.com
• Join DataTalks.Club: https://datatalks.club/slack.html
• Our events: https://datatalks.club/events.html

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We talked about:

• Christoph’s background
• Kaggle and other competitions
• How Christoph became interested in interpretable machine learning
• Interpretability vs Accuracy
• Christoph’s current competition engagement
• How Christoph chooses topics for books
• Why Christoph started the writing journey with a book
• Self-publishing vs via a publisher
• Christoph’s other books
• What is conformal prediction?
• Christoph’s book on SHAP
• Explainable AI vs Interpretable AI
• Working alone vs with other people
• Christoph’s other engagements and how to stay hands-on
• Keeping a logbook
• Does one have to be an expert on the topic to write a book about it?
• Writing in the open and other feedback gathering methods
• Advice for those who want to be technical writers
• Self-publishing tools
• Finding Christoph online

Links:

• LinkedIn: https://www.linkedin.com/in/christoph-molnar/
• Website: https://christophmolnar.com/

Free ML Engineering course: http://mlzoomcamp.comJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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We talked about:

• Jack’s background
• Transitioning from IC to management
• Lesson not taught in traditional school
• The importance of people’s perception, trust, and respect
• How soft skills are relevant to machine learning
• How to put on a salesman hat in machine learning management
• The importance of visuals and building a POC as fast as possible
• 1st Rule of Machine Learning – don’t be afraid to start without machine learning
• The importance of understanding the reality that data represents
• The importance of putting yourself in the shoes of customers
• The importance of software engineering skills in machine learning
• Where to find Jack’s content
• Jack’s next venture

Links:

• Jack's LinkedIn profile: https://www.linkedin.com/in/jackblandin/

Free ML Engineering course: http://mlzoomcamp.comJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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Links:

• Mini sound bath: https://www.youtube.com/watch?v=g-lDrcSqcrQ

Free ML Engineering course: http://mlzoomcamp.comJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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We talked about:

• Lera’s background
• Lera’s move from Ukraine to Germany
• The transition from Marketing to Product Ownership
• The importance of communication and one-on-ones
• The role of Product Owner
• Utilizing Scrum as a Product Owner
• Building teams and cross-functionality
• Lera’s experience learning about search
• The importance of having both technical knowledge and business context
• Open developer positions at AUTODOC
• What experience Lera came to AUTODOC with
• How marketing skills helped Lera in her current role
• Lera’s resource recommendations
• Everything is possible

Links:

• Post: https://www.linkedin.com/posts/leracaiman_elasticsearch-ecommerce-activity-7106615081588674560-5WQO

Free ML Engineering course: http://mlzoomcamp.comJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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Links:

• LinkedIn: https://www.linkedin.com/in/ioannis-mesionis/
• Github: https://github.com/ioannismesionis
• Website: https://ioannismesionis.github.io/

Free ML Engineering course: http://mlzoomcamp.comJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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Free ML Engineering course: http://mlzoomcamp.comJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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Free ML Engineering course: http://mlzoomcamp.comJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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We talked about:

• Angela's background
• Angela's role at Sam's Club
• The usefulness of knowing ML as a data engineer
• Angela's career path
• Transitioning from data analyst to data engineer/system designer
• Best practices for system design and data engineering
• Working with document databases
• Working with network-based databases
• Detecting fraud with a network-based database
• Selecting the database type to work with
• Neo4j vs Postgres
• The importance of having software engineering knowledge in data engineering
• Data quality check tooling
• The greatest challenges in data engineering
• Debugging and finding the root cause of a failed job
• What kinds of tools Angela uses on a daily basis
• Working with external data sources
• Angela's resource recommendations

Links:

• LinkedIn: https://www.linkedin.com/in/aramirez1305/
• Twitter: https://twitter.com/angelamaria__r
• Github: https://github.com/aramir62
• Previous podcast talk: https://twitter.com/i/spaces/1OwGWwZAZDnGQ?s=20

Free ML Engineering course: http://mlzoomcamp.com

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Loïc's background
• Data management
• Loïc's transition to data engineer
• Challenges in the transition to data engineering
• What is a data architect?
• The output of a data architect's work
• Establishing metrics and dimensions
• The importance of communication
• Setting up best practices for the team
• Staying relevant and tech-watching
• Setting up specifications for a pipeline
• Be agile, create a POC, iterate ASAP, and build reusable templates
• Reaching out to Loïc for questions

Links:

• Loiic LinkedIn: https://www.linkedin.com/in/loicmagnien/

Free ML Engineering course: http://mlzoomcamp.com

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

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Links:

• The Book of Why: https://amzn.to/3OZpvBk
• Causal Inference and Discovery in Python: https://amzn.to/46Pperr
• Book's GitHub repo: https://github.com/PacktPublishing/Causal-Inference-and-Discovery-in-Python
• The Battle of Giants: Causality vs NLP (PyData Berlin 2023): https://www.youtube.com/watch?v=Bd1XtGZhnmw
• New Frontiers in Causal NLP (papers repo): https://bit.ly/3N0TFTL

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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Links:

• LinkedIn: https://www.linkedin.com/in/jmssalas
• Github: https://github.com/jmssalas
• Website & Newsletter: https://jmssalas.com

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

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We talked about:

• Sandra's background
• Making a YouTube channel to break into the LLM space
• The business cases for LLMs
• LLMs as amplifiers
• The befits of keeping a human in the loop when using LLMs (AI limitations)
• Using LLMs as assistants
• Building an app that uses an LLM
• Prompt whisperers and how to improve your prompts
• Sandra's 7-day LLM experiment
• Sandra's LLM content recommendations
• Finding Sandra online

Links:

• LinkedIn: https://www.linkedin.com/in/sandrakublik/
• Twitter: https://twitter.com/sandra_kublik
• Youtube: https://www.youtube.com/@sandra_kublik

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

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Links:

• Website: https://www.titanml.co/
• Beta docs: https://titanml.gitbook.io/iris-documentation/overview/guide-to-titanml...
• Using llama2.0 in TitanML Blog: https://medium.com/@TitanML/the-easiest-way-to-fine-tune-and-inference-llama-2-0-8d8900a57d57
• Discord: https://discord.gg/83RmHTjZgf
• Meryem LinkedIn: https://www.linkedin.com/in/meryemarik/

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

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We talked about:

• Bela's background
• Why startups even need investors
• Why open source is a viable go-to-market strategy
• Building a bottom-up community
• The investment thesis for the TKM Family Office and the blurriness of the funding round naming convention
• Angel investors vs VC Funds vs family offices
• Bela's investment criteria and GitHub stars as a metric
• Inbound sourcing, outbound sourcing, and investor networking
• Making a good impression on an investor
• Balancing open and closed source parts of a product
• The future of open source
• Recent successes of open source companies
• Bela's resource recommendations

Links:

• Understand who is engaging with your open source project article: https://www.crowd.dev/
• Top 6 Books on Developer Community Building: https://www.crowd.dev/post/top-6-books-on-developer-community-building
• Which open source software metrics matter: https://www.bvp.com/atlas/measuring-the-engagement-of-an-open-source-software-community#Which-open-source-software-metrics-matter

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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Links:

• Book: https://www.manning.com/books/machine-learning-system-design?utm_source=AGMLBookcamp&utm_medium=affiliate&utm_campaign=book_babushkin_machine_4_25_23&utm_content=twitter
• Discount: poddatatalks21 (35% off)
• Evidently: https://www.evidentlyai.com/
• Article: https://medium.com/people-ai-engineering/design-documents-for-ml-models-bbcd30402ff7

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Polina's background
• How common it is for PhD students to build ML pipelines end-to-end
• Simultaneous PhD and industry experience
• Support from both the academic and industry sides
• How common the industrial PhD setup is and how to get into one
• Organizational trust theory
• How price relates to trust
• How trust relates to explainability
• The importance of actionability
• Explainability vs interpretability vs actionability
• Complex glass box models
• Does the explainability of a model follow explainability?
• What explainable AI bring to customers and end users
• Can all trust be turned into KPI?

Links:

• LinkedIn: https://www.linkedin.com/in/polina-mosolova/
• Neural Additive Models paper: https://proceedings.neurips.cc/paper/2021/file/251bd0442dfcc53b5a761e050f8022b8-Paper.pdf
• Neural Basis Model paper: https://arxiv.org/pdf/2205.14120.pdf
• Interpretable Feature Spaces paper: https://kdd.org/exploration_files/vol24issue1_1._Interpretable_Feature_Spaces_revised.pdf

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We talked about:

• Simon's background
• What MLOps is and what it isn't
• Skills needed to build an ML platform that serves 100s of models
• Ranking the importance of skills
• The point where you should think about building an ML platform
• The importance of processes in ML platforms
• Weighing your options with SaaS platforms
• The exploratory setup, experiment tracking, and model registry
• What comes after deployment?
• Stitching tools together to create an ML platform
• Keeping data governance in mind when building a platform
• What comes first – the model or the platform?
• Do MLOps engineers need to have deep knowledge of how models work?
• Is API design important for MLOps?
• Simon's recommendations for furthering MLOps knowledge

Links:

• LinkedIn: https://www.linkedin.com/in/simonstiebellehner/
• Github: https://github.com/stiebels
• Medium: https://medium.com/@sistel

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Santona's background
• Focusing on data workflows
• Upsolver vs DBT
• ML pipelines vs Data pipelines
• MLOps vs DataOps
• Tools used for data pipelines and ML pipelines
• The “modern data stack” and today's data ecosystem
• Staging the data and the concept of a “lakehouse”
• Transforming the data after staging
• What happens after the modeling phase
• Human-centric vs Machine-centric pipeline
• Applying skills learned in academia to ML engineering
• Crafting user personas based on real stories
• A framework of curiosity
• Santona's book and resource recommendations

Links:

• LinkedIn: https://www.linkedin.com/in/santona-tuli/
• Upsolver website: upsolver.com
• Why we built a SQL-based solution to unify batch and stream workflows: https://www.upsolver.com/blog/why-we-built-a-sql-based-solution-to-unify-batch-and-stream-workflows

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Hugo's background
• Why do tools and the companies that run them have wildly different names
• Hugo's other projects beside Metaflow
• Transitioning from educator to DevRel
• What is DevRel?
• DevRel vs Marketing
• How DevRel coordinates with developers
• How DevRel coordinates with marketers
• What skills a DevRel needs
• The challenges that come with being an educator
• Becoming a good writer: nature vs nurture
• Hugo's approach to writing and suggestions
• Establishing a goal for your content
• Choosing a form of media for your content
• Is DevRel intercompany or intracompany?
• The Vanishing Gradients podcast
• Finding Hugo online

Links:

• Hugo Browne's github: http://hugobowne.github.io/
• Vanishing Gradients: https://vanishinggradients.fireside.fm/
• MLOps and DevOps: Why Data Makes It Differenthttps://www.oreilly.com/radar/mlops-and-devops-why-data-makes-it-different/
• Evaluate Metaflow for free, right from your Browser: https://outerbounds.com/sandbox/

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about;

• Antonis' background
• The pros and cons of working for a startup
• Useful skills for working at a startup and the Lean way to work
• How Antonis joined the DataTalks.Club community
• Suggestions for students joining the MLOps course
• Antonis contributing to Evidently AI
• How Antonis started freelancing
• Getting your first clients on Upwork
• Pricing your work as a freelancer
• The process after getting approved by a client
• Wearing many hats as a freelancer and while working at a startup
• Other suggestions for getting clients as a freelancer
• Antonis' thoughts on the Data Engineering course
• Antonis' resource recommendations

Links:

• Lean Startup by Eric Ries: https://theleanstartup.com/
• Lean Analytics: https://leananalyticsbook.com/
• Designing Machine Learning Systems by Chip Huyen: https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/
• Kafka Streaming with python by Khris Jenkins tutorial video: https://youtu.be/jItIQ-UvFI4

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

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We talked about:

• Bart's background
• What is data governance?
• Data dictionaries and data lineage
• Data access management
• How to learn about data governance
• What skills are needed to do data governance effectively
• When an organization needs to start thinking about data governance
• Good data access management processes
• Data masking and the importance of automating data access
• DPO and CISO roles
• How data access management works with a data mesh approach
• Avoiding the role explosion problem
• The importance of data governance integration in DataOps
• Terraform as a stepping stone to data governance
• How Raito can help an organization with data governance
• Open-source data governance tools

Links:

• LinkedIn: https://www.linkedin.com/in/bartvandekerckhove/
• Twitter: https://twitter.com/Bart_H_VDK
• Github: https://github.com/raito-io
• Website: https://www.raito.io/
• Data Mesh Learning Slack: https://data-mesh-learning.slack.com/join/shared_invite/zt-1qs976pm9-ci7lU8CTmc4QD5y4uKYtAA#/shared-invite/email
• DataQG Website: https://dataqg.com/
• DataQG Slack: https://dataqgcommunitygroup.slack.com/join/shared_invite/zt-12n0333gg-iTZAjbOBeUyAwWr8I~2qfg#/shared-invite/email
• DMBOK (Data Management Book of Knowledge): https://www.dama.org/cpages/body-of-knowledge
• DMBOK Wheel describing the data governance activities: https://www.dama.org/cpages/dmbok-2-wheel-images

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Boyan's background
• What is data strategy?
• Due diligence and establishing a common goal
• Designing a data strategy
• Impact assessment, portfolio management, and DataOps
• Data products
• DataOps, Lean, and Agile
• Data Strategist vs Data Science Strategist
• The skills one needs to be a data strategist
• How does one become a data strategist?
• Data strategist as a translator
• Transitioning from a Data Strategist role to a CTO
• Using ChatGPT as a writing co-pilot
• Using ChatGPT as a starting point
• How ChatGPT can help in data strategy
• Pitching a data strategy to a stakeholder
• Setting baselines in a data strategy
• Boyan's book recommendations

Links:

• LinkedIn: https://www.linkedin.com/in/angelovboyan/
• Twitter: https://twitter.com/thinking_code
• Github: https://github.com/boyanangelov
• Website: https://boyanangelov.com/

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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We talked about:

• Katharine's background
• Katharine's ML privacy startup
• GDPR, CCPA, and the “opt-in as the default” approach
• What is data privacy?
• Finding Katharine's book – Practical Data Privacy
• The various definitions of data privacy and “user profiles”
• Privacy engineering and privacy-enhancing technologies
• Why data privacy is important
• What is differential privacy?
• The importance of keeping privacy in mind when designing systems
• Data privacy on the example of ChatGPT
• Katharine's resource suggestions for learning about data privacy

Links:

• LinkedIn: https://www.linkedin.com/in/katharinejarmul/
• Twitter: https://twitter.com/kjam

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcampJoin DataTalks.Club: https://datatalks.club/slack.htmlOur events: https://datatalks.club/events.html

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We talked about:

• Arseny's background
• Working on machine learning in startups
• What is Machine Learning System Design?
• Constraints and requirements
• Known unknowns vs unknown unknowns (Design stage)
• Writing a design document
• Technical problems vs product-oriented problems
• The solution part of the Design Document
• What motivated Arseny to write a book on ML System Design
• Examples of a Design Document in the book
• The types of readers for ML System Design
• Working with the co-author
• Reacting to constraints and feedback when writing a book
• Arseny's favorite chapter of the book
• Other resources where you can learn about ML System Design
• Twitter Giveaway

Links:

• Book: https://www.manning.com/books/machine-learning-system-design?utm_source=AGMLBookcamp&utm_medium=affiliate&utm_campaign=book_babushkin_machine_4_25_23&utm_content=twitter
• Discount: poddatatalks21 (35% off)

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Johannes’s background
• Johannes’s Open Source Spotlight demos – Refinery and Bricks
• The difficulties of working with natural language processing (NLP)
• Incorporating ChatGPT into a process as a heuristic
• What is Bricks?
• The process of starting a startup – Kern
• Making the decision to go with open source
• Pros and cons of launching as open source
• Kern’s business model
• Working with enterprises
• Johannes as a salesperson
• The team at Kern
• Johannes’s role at Kern
• How Johannes and Henrik separate responsibilities at Kern
• Working with very niche use cases
• The short story of how Kern got its funding
• Johannes’s resource recommendation

Links:

• Refinery's GitHub repo: https://github.com/code-kern-ai/refinery
• Bricks' Github repo: https://github.com/code-kern-ai/bricks
• Bricks Open Source Spotlight demo: https://www.youtube.com/watch?v=r3rXzoLQy2U
• Refinery Open Source Spotlight demo: https://www.youtube.com/watch?v=LlMhN2f7YDg
• Discord: https://discord.com/invite/qf4rGCEphW
• Ker's Website: https://www.kern.ai

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Rosona’s background
• How mathematics knowledge helps in industry
• What is industrial data?
• Setting up an industrial process using blue paint
• Internet companies’ data vs industrial data
• Explaining industrial processes using packing peanuts
• Why productive industry needs data
• Measuring product qualities
• How data specialists use industrial data
• Defining and measuring sustainability
• Using data in reactionary measures to changing regulations
• Types of industrial data
• Solving problems and optimizing with industrial data
• Industrial solvers
• Tiny data vs Big data in productive industry
• The advantages of coming from academia into productive industry
• Materials and resources for industrial data
• Women in industry
• Why Rosona decided to shift to industrial data

Links:

• Kaggle dataset: https://www.kaggle.com/datasets/paresh2047/uci-semcom

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We talked about:

• Aaisha’s background
• How homeschooling affects self-study
• Deciding on what to learn about
• Establishing whether a resource is good
• How Aaisha focuses on learning
• Deciding on what kind of project to build
• Find research materials
• Aaisha’s experience with the Data Talks Club ML Zoomcamp
• ML Zoomcamp projects
• Aaisha’s interest in bioinformatics
• Keeping motivated with deadlines
• Notes and time-tracking tools
• Drawbacks to self-studying
• Aaisha’s interest in machine learning
• Aaisha’s least favorable part of ML Zoomcamp
• Helping people as a way to learn
• Using ChatGPT as a “study group”
• Is it possible to use self-studying to learn high-level topics
• Switching topics to avoid burnout
• Aaisha’s resource recommendations

Links:

• LinkedIn: https://www.linkedin.com/in/aaisha-muhammad/
• Twitter: https://twitter.com/ZealousMushroom
• Github: https://github.com/AaishaMuhammad
• Website: http://www.aaishamuhammad.co.za/

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Shir’s background
• Debrief culture
• The responsibilities of a group manager
• Defining the success of a DS manager
• The three pillars of data science management
• Managing up
• Managing down
• Managing across
• Managing data science teams vs business teams
• Scrum teams, brainstorming, and sprints
• The most important skills and strategies for DS and ML managers
• Making sure proof of concepts get into production

Links:

• The secret sauce of data science management: https://www.youtube.com/watch?v=tbBfVHIh-38
• Lessons learned leading AI teams: https://blogs.intuit.com/2020/06/23/lessons-learned-leading-ai-teams/
• How to avoid conflicts and delays in the AI development process (Part I): https://blogs.intuit.com/2020/12/08/how-to-avoid-conflicts-and-delays-in-the-ai-development-process-part-i/
• How to avoid conflicts and delays in the AI development process (Part II): https://blogs.intuit.com/2021/01/06/how-to-avoid-conflicts-and-delays-in-the-ai-development-process-part-ii/
• Leading AI teams deck: https://drive.google.com/drive/folders/1_CnqjugtsEbkIyOUKFHe48BeRttX0uJG
• Leading AI teams video: https://www.youtube.com/watch?app=desktop&v=tbBfVHIh-38

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Nadia’s background
• Academic research in software engineering
• Design patterns
• Software engineering for ML systems
• Problems that people in industry have with software engineering and ML
• Communication issues and setting requirements
• Artifact research in open source products
• Product vs model
• Nadia’s open source product dataset
• Failure points in machine learning projects
• Finding solutions to issues using Nadia’s dataset and experience
• The problem of siloing data scientists and other structure issues
• The importance of documentation and checklists
• Responsible AI
• How data scientists and software engineers can work in an Agile way

Links:

• Model Card: https://arxiv.org/abs/1810.03993
• Datasheets: https://arxiv.org/abs/1803.09010
• Factsheets: https://arxiv.org/abs/1808.07261
• Research Paper: https://www.cs.cmu.edu/~ckaestne/pdf/icse22_seai.pdf
• Arxiv version: https://arxiv.org/pdf/2110.

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Aleksander’s background
• The difficulty of selling data stack as a service
• How Aleksander got into consulting
• The Mom Test – extracting feedback from people
• User interviews
• Why Aleksander’s data stack as a service startup was not viable
• How Aleksander decided to switch to consulting
• Finding clients to consult
• Figuring out how to position your services
• Geographical limitations
• Figuring out your target audience
• The importance of networking and marketing
• Pricing your services
• The pitfalls of daily and hourly pricing and how to balance incentives
• Is Germany a good place to found a company?
• Aleksander’s book recommendations

Links:

• LinkedIn: https://www.linkedin.com/in/alkrusz/
• Twitter: https://twitter.com/alkrusz
• Website: www.leukos.io

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Ruslan’s background
• Fighting procrastination and perfectionism
• What is biohacking?
• The role of dopamine and other hormones in daily life
• How meditation can help
• The influence light has on our bodies
• Behavioral biohacking
• Daylight lamps and using light to wake up
• Sleep cycles
• How nutrition affects productivity
• Measuring productivity
• Examples of unsuccessful biohacking attempts
• Stoicism, voluntary discomfort, and self-challenges
• Biohacking risks and ways to prevent them
• Coffee and tea biohacking
• Using self-reflection and tracking to measure results
• Mindset shifting
• Stoicism book recommendation
• Work/life balance
• Ruslan’s biohacking resource recommendation

Links:

• LinkedIn: https://www.linkedin.com/in/ruslanshchuchkin/

ree data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Parvathy’s background
• Brainstorming sessions with nonprofits to establish data maturity
• Example of an Analytics for a Better World project
• The overall data maturity situation of nonprofits vs private sector
• Solving the skill gap
• Publicly available content
• The Analytics for a Better World Academy
• The Academy’s target audience
• How researchers can work with Analytics for a Better World
• Improving data maturity in nonprofit organizations
• People, processes, and technology
• Typical tools that Analytics for a Better World recommends to nonprofits
• Profiles in nonprofits
• Does Analytics for a Better World has a need for data engineers?
• The Analytics for a Better World team
• Factors that help organizations become more data-driven
• Parvathy’s resource recommendations

Links:

• LinkedIn: https://www.linkedin.com/in/parvathykrishnank/
• Twitter:  https://twitter.com/ABWInstitute
• Github: https://github.com/Analytics-for-a-Better-World
• Website:  https://analyticsbetterworld.org/

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Dania’s background
• Founding the AI Guild
• Datalift Summit
• Coming up with meetup topics
• Diversity in Berlin
• Other types of diversity besides gender
• The pitfalls of lacking diversity
• Creating an environment where people can safely share their experiences
• How the AI Guild helps organizations become more diverse
• How the AI guild finds women in the fields of AI and data science
• Advice for people in underrepresented groups
• Organizing a welcoming environment and creating a code of conduct
• AI Guild’s consulting work and community
• AI Guild team
• Dania’s resource recommendations
• Upcoming Datalift Summit

Links:

• Call for Speakers for the #datalift summit (Berlin, 14 to 16 June 2023): https://eu1.hubs.ly/H02RXvX0
• Coded Bias documentary on Netflix: https://www.netflix.com/de/title/81328723#:~:text=This%20documentary%20investigates%20the%20bias,flaws%20in%20facial%20recognition%20technology.
• Book Weapons of Math Destruction by Cathy O'Neil: https://en.wikipedia.org/wiki/Weapons_of_Math_Destruction
• Book Lean In by Sheryl Sandberg: https://en.wikipedia.org/wiki/Lean_In

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Tatiana’s background
• Going from academia to healthcare to the tech industry
• What staff engineers do
• Transferring skills from academia to industry and learning new ones
• The importance of having mentors
• Skipping junior and mid-level straight into the staff role
• Convincing employers that you can take on a lead role
• Seeing failure as a learning opportunity
• Preparing for coding interviews
• Preparing for behavioral and system design interviews
• The importance of having a network and doing mock interviews
• How much do staff engineers work with building pipelines, data science, ETC, MPOps, etc.?
• Context switching
• Advice for those going from academia to industry
• The most exciting thing about working as an AI staff engineer
• Tatiana’s book recommendations

Links:

• LinkedIn: https://www.linkedin.com/in/tatigabru/
• Twitter:  https://twitter.com/tatigabru
• Github: https://github.com/tatigabru
• Website:  http://tatigabru.com/

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Jekaterina’s background
• How Jekaterina started freelancing
• Jekaterina’s initial ways of getting freelancing clients
• How being a generalist helped Jekaterina’s career
• Connecting business and data
• How Jekaterina’s LinkedIn posts helped her get clients
• Jekaterina’s work in fundraising
• Cohorts and KPIs
• Improving communication between the data and business teams
• Motivating every link in the company’s chain
• The cons of freelancing
• Balancing projects and networking
• The importance of enjoying what you do
• Growing the client base
• In the office work vs working remotely
• Jekaterina’s advice who people who feel stuck
• Jekaterina’s resource recommendations

Links:

• Jekaterina's LinkedIn: https://www.linkedin.com/in/jekaterina-kokatjuhha/

Join DataTalks.Club: https://datatalks.club/slack.html

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We talked about

• Chris’s background
• Switching careers multiple times
• Freedom at companies
• Chris’s role as an internal consultant
• Chris’s sabbatical
• ChatGPT
• How being a generalist helped Chris in his career
• The cons of being a generalist and the importance of T-shaped expertise
• The importance of learning things you’re interested in
• Tips to enjoy learning new things
• Recruiting generalists
• The job market for generalists vs for specialists
• Narrowing down your interests
• Chris’s book recommendations

Links:

• Lex Fridman: science, philosophy, media, AI (especially earlier episodes): https://www.youtube.com/lexfridman
• Andrej Karpathy, former Senior Director of AI at Tesla, who's now focused on teaching and sharing his knowledge: https://www.youtube.com/@AndrejKarpathy
• Beautifully done videos on engineering of things in the real world: https://www.youtube.com/@RealEngineering
• Chris' website: https://szafranek.net/
• Zalando Tech Radar: https://opensource.zalando.com/tech-radar/
• Modal Labs, new way of deploying code to the cloud, also useful for testing ML code on GPUs: https://modal.com
• Excellent Twitter account to follow to learn more about prompt engineering for ChatGPT: https://twitter.com/goodside
• Image prompts for Midjourney: https://twitter.com/GuyP
• Machine Learning Workflows in Production - Krzysztof Szafanek: https://www.youtube.com/watch?v=CO4Gqd95j6k
• From Data Science to DataOps: https://datatalks.club/podcast/s11e03-from-data-science-to-dataops.html

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Luke’s background
• Luke’s podcast - AI Game Changers
• How Luke helps people get jobs
• What’s changed in the recruitment market over the last 6 months
• Getting ready for the interview process
• Stage “zero” – the filter between the candidate and the company
• Preparing for the introduction stage – research and communication
• Reviewing the fundamentals during preparation
• Preparing for the technical part of the interview
• Establishing the hiring company’s expectations
• Depth vs breadth
• Overly theoretical and mathematical questions in interviews
• Bombing (failing) in the middle of an interview
• Applying to different roles within the same company
• Luke’s resource recommendations

Links:

• Luke's LinkedIn: https://www.linkedin.com/in/lukewhipps/

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Pauline’s background
• Pauline’s work as a manager at IBM
• What is indie hacking?
• Pauline initial indie hacking projects
• Getting ready for launch
• Responsibilities and challenges in indie hacking
• Pauline’s latest indie hacking project
• Going live and marketing
• Challenges with Unreal Me
• Staying motivated with indie hacking projects
• Skills Pauline picked up while doing indie hacking projects
• Balancing a day job and indie hacking
• Micro SaaS and AboutStartup.io
• How Pauline comes up with ideas for projects
• Going from an idea on paper to building a project
• Pauline’s Twitter success
• Connecting with Pauline online
• Pauline’s indie hacking inspiration
• Pauline’s resource recommendation

Links:

• Website: https://wintopy.io/
• Pauline's Twitter: https://twitter.com/Pauline_Cx
• Pauline's LinkedIn: https://www.linkedin.com/in/paulineclavelloux/
• Blog about Indiehacking: https://aboutstartup.io

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Johanna’s background
• Open science course and reproducible papers
• Research software engineering
• Convincing a professor to work on software instead of papers
• The importance of reproducible analysis
• Why academia is behind on software engineering
• The problems with open science publishing in academia
• The importance of standard coding practices
• How Johanna got into research software engineering
• Effective ways of learning software engineering skills
• Providing data and analysis for your project
• Johanna’s initial experience with software engineering in a project
• Working with sensitive data and the nuances of publishing it
• How often Johanna does hackathons, open source, and freelancing
• Social media as a source of repos and Johanna’s favorite communities
• Contributing to Git repos
• Publishing in the open in academia vs industry
• Johanna’s book and resource recommendations
• Conclusion

Links:

• The Society of Research Software Engineering, plus regional chapters: https://society-rse.org/
• The RSE Association of Australia and New Zealand: https://rse-aunz.github.io/
• Research Software Engineers (RSEs) The people behind research software: https://de-rse.org/en/index.html
• The software sustainability institute: https://www.software.ac.uk/
• The Carpentries (beginner git and programming courses): https://carpentries.org/
• The Turing Way Book of Reproducible Research: https://the-turing-way.netlify.app/welcome

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Marysia’s background
• What data-centric AI is
• Data-centric Kaggle competitions
• The mindset shift to data-centric AI
• Data-centric does not mean you should not iterate on models
• How to implement the data-centric approach
• Focusing on the data vs focusing on the model
• Resources to help implement the data-centric approach
• Data-centric AI vs standard data cleaning
• Making sure your data is representative
• Knowing when your data is good enough
• The importance of user feedback
• “Shadow Mode” deployment
• What to do if you have a lot of bad data or incomplete data
• Marysia’s role at PyData
• How Marysia joined PyData
• The difference between PyData and PyCon
• Finding Marysia online

Links:

• Embetter & Bulk Demo: https://www.youtube.com/watch?v=L---nvDw9KU

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Loris’ background
• Transitioning from physics to data
• Aligning people on concepts
• Lead indicators and stickiness
• Context, semantics, and meaning
• Communication and being memorable
• Making data digestible for business and building trust
• The importance of understanding the language of business
• Stakeholder mapping
• Attending business meetings as a data professional
• Organizing your stakeholder map
• Prioritizing
• How to support the business strategy
• Learning to speak online
• Resource recommendations from Loris

Links:

• Discovering Data Discord server: https://bit.ly/discovering-data-discord
• Loris' LinkedIn: https://www.linkedin.com/in/lorismarini/
• Loris' Twitter: https://twitter.com/LorisMarini

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We talked about:

• Sadat’s background
• Sadat’s backend engineering experience
• Sadat’s pivot point as a backend engineer
• Sadat’s exposure to ML and Data Science
• Sadat’s Act Before you Think approach (with safety nets)
• Sadat’s street cred and transition into management
• The hiring process as an internal candidate
• The importance of people management skills
• The Brag List
• The most difficult part of transitioning to management
• Focusing on projects and setting milestones
• Sadat’s transition from EM to data science management
• How much domain knowledge is needed for management?
• The main difference between engineering and management
• How being an EM helped Sadat transition no DS management
• 53:32 Transitioning to DS management from other roles
• How to feel accomplished as a manager
• Sadat’s book recommendations
• Sadat’s meetups

Links:

• Sadat's Meetup page: https://www.meetup.com/berlin-search-technology-meetup/
• Meetup event "Bias in AI: how to measure it and how to fix it event": https://www.meetup.com/data-driven-ai-berlin-meetup/events/289927565/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Irina’s background
• Irina as a mentor
• Designing curriculum and program management at AI Guild
• Other things Irina taught at AI Guild
• Why Irina likes teaching
• Students’ reluctance to learn cloud
• Irina as a manager
• Cohort analysis in a nutshell
• How Irina started teaching formally
• Irina’s diversity project in the works
• How DataTalks.Club can attract more female students to the Zoomcamps
• How to get technical feedback at work
• Antipatterns and overrated/overhyped topics in data analytics
• Advice for young women who want to get into data science/engineering
• Finding Irina online
• Fundamentals for data analysts
• Suggestions for DataTalks.club collaborations
• Conclusions

Links:

• LinkedIn Account: https://www.linkedin.com/in/irinabrudaru/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Angelica’s background
• Angelica’s books
• Data journalism
• How Angelica got into data journalism
• The field of digital humanities and Angelica’s data journalism course
• Technical articles vs data journalism articles
• Transforming reports into data storytelling
• Are reports to stakeholders considered technical writing?
• Data visualization in articles
• Article length
• The process of writing an article
• Finding writing topics
• How Angelica got into writing a book (communication with publishers)
• The process for writing a book
• Brainstorming
• Reviews and revisions
• Conclusion

Links:

• Data Journalism examples (FENCED OUT): https://www.washingtonpost.com/graphics/world/border-barriers/europe-refugee-crisis-border-control/??noredirect=on
• Data Journalism examples (La tierra esclava): https://latierraesclava.eldiario.es/
• Small medium publication aiming at being Stack Overflow of Medium: https://medium.com/syntaxerrorpub
• Example of a self-published book on Data Visualization: https://www.amazon.com/Introduction-Data-Visualization-Storytelling-Scientist-ebook/dp/B07VYCR3Z6/ref=sr_1_4?crid=4JRJ48O7K8TK&keywords=joses+berengueres&qid=1668270728&sprefix=joses+beremguere%2Caps%2C273&sr=8-4
• My novels (in Italian) La bambina e il Clown: https://www.amazon.it/Bambina-Clown-Angelica-Lo-Duca/dp/1500984515/ref=sr_1_9?__mk_it_IT=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=2KGK9GMN0FAHI&keywords=la+bambina+e+il+clown&qid=1668270769&sprefix=la+bambina+e+il+clown%2Caps%2C88&sr=8-9
• My novels (in Italian) Il Violinista: https://www.amazon.it/Violinista-1-Angelica-Lo-Duca/dp/1501009672/ref=sr_1_1?__mk_it_IT=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=12KTF9EF5UKIG&keywords=il+violinista+lo+duca&qid=1668270791&sprefix=il+violinista+lo+duca%2Caps%2C81&sr=8-1
• Course on Data Journalism: https://www.coursera.org/learn/visualization-for-data-journalism

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Nikola’s background
• Making the first steps towards a transition to BI and Analytics Engineering
• Learning the skills necessary to transition to Analytics Engineering
• The in-between period – from Marketing to Analytics Engineering
• Nikola’s current responsibilities
• Understanding what a Data Model is
• Tools needed to work as an Analytics Engineer
• The Analytics Engineering role over time
• The importance of DBT for Analytics Engineers
• Where can one learn about data modeling theory?
• Going from Ancient Greek and Latin to understanding Data (Just-In-Time Learning)
• The importance of having domain knowledge to analytics engineering
• Suggestion for those wishing to transition into analytics engineering
• The importance of having a mentor when transitioning
• Finding a mentor
• Helpful newsletters and blogs
• Finding Nikola online

Links:

• Nikola's LinkedIn account: https://www.linkedin.com/in/nikola-maksimovic-40188183/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• About Anna and METRO
• Anna’s background
• The importance of a technical background for data product owners
• What are product owners?
• Product owners vs product managers
• Anna’s work on recommender systems at METRO
• Expanding the data team
• Types of algorithms used for recommender systems
• What kind of knowledge and skills data product owners need to have
• Problems and ideas should come from the business
• How Anna handles all her responsibilities
• The process for starting work on new domains
• Product portfolio management
• ProductTank and Anna’s role in it
• Anna’s resource recommendations

Links:

• Data Science for Business Book: https://www.amazon.de/-/en/Foster-Provost/dp/1449361323/ref=sr_1_1?keywords=data+science+for+business&qid=1666404807&qu=eyJxc2MiOiIxLjg3IiwicXNhIjoiMS41MiIsInFzcCI6IjEuNDYifQ%3D%3D&sr=8-1
• Article on Data Science Products: https://www.linkedin.com/pulse/way-create-data-science-products-lessons-learnt-anna-hannemann-phd/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Audience Poll
• Andrey’s background
• What data science practice is
• Best DS practice in a traditional company vs IT-centric companies
• Getting started with building data science practice (finding out who you report to)
• Who the initiative comes from
• Finding out what kind of problems you will be solving (Centralized approach)
• Moving to a semi-decentralized approach
• Resources to learn about data science practice
• Pivoting from the role of a software engineer to data scientist
• The most impactful realization from data science practice
• Advice for individual growth
• Finding Andrey online

Links:

• Data Teams book: https://www.amazon.com/Data-Teams-Management-Successful-Data-Focused/dp/1484262271/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Sonal’s background
• How the idea for Zingg came about
• What Zingg is
• The difference between entity resolution and identity resolution
• How duplicate detection relates to entity resolution
• How Sonal decided to start working on Zingg
• How Zingg works
• What Zingg runs on
• Switching from consultancy to working on a new open source solution
• Why Zingg is open source
• Open source licensing
• Working on Zingg initially vs now
• Zingg’s current and future team
• Sonal’s biggest current challenge
• Avoiding problems with entity/identity resolution through database design
• Identity resolution vs basic joins, data fusions, and fuzzy joins
• Deterministic matching vs probabilistic machine learning
• Identity and entity resolution applications for fraud detection
• Graph algorithms vs classic ML in entity resolution
• Identity resolution success stories
• What Sonal would do differently given the chance to start over with Zingg
• Advice for those seeking to realize their own solution to a data problem
• Reading suggestion from Sonal
• Conclusion

Links:

• Open-Source Spotlight demo "Zingg":https://www.youtube.com/watch?v=zOabyZxN9b0
• Creative Selection: Inside Apple's Design Process During the Golden Age of Steve Jobs book: https://www.amazon.com/Creative-Selection-Inside-Apples-Process/dp/1250194466

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Tomasz’s background
• What Tomasz did before DataOps (Data Science)
• Why Tomasz made the transition from Data science to DataOps
• What is DataOps?
• How is DataOps related to infrastructure?
• How Tomasz learned the skills necessary to become DataOps
• Becoming comfortable with terminal
• The overlap between DataOps and Data Engineering
• Suitable/useful skills for DataOps
• Minimal operational skills for DataOps
• Similarities between DataOps and Data Science Managers
• Tomasz’s interesting projects
• Confidence in results and avoiding going too deep with edge cases
• Conclusion

Links:

• Terminal setup video, 19 minutes long: https://www.youtube.com/watch?v=D2PSsnqgBiw
• Command line videos, one and a half hour to become somewhat comfy with the terminal: https://www.youtube.com/playlist?list=PLIhvC56v63IKioClkSNDjW7iz-6TFvLwS
• Course from MIT talking about just that (command line, git, storing secrets): https://missing.csail.mit.edu/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Katie’s background
• What is a data scientist?
• What is a data science manager?
• Quality of the craft
• How data leaders promote career growth
• Supporting senior data professionals
• Choosing the IC route vs the management route
• Managing junior data professionals
• Talking to senior stakeholders and PMs as a junior
• The importance of hiring juniors
• What skills do data scientist managers need to get hired?
• How juniors that are just starting out can set themselves apart from the competition
• Asking senior colleagues for help and the rubber duck channel
• The challenges of the head of data
• Conclusion

Links:

• Jobs at Gloss Genius: https://boards.greenhouse.io/glossgenius

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Alvaro’s background
• Working as a QA (Quality Assurance) engineer
• Transitioning from QA to Machine Learning
• Gathering knowledge about ML field
• Searching for an ML job (improving soft skills and CV)
• Data science interview skills
• Zoomcamp projects
• Zoomcamp project deployment
• How to not undersell yourself during interviews
• Alvaro’s experience with interviews during his transition
• Alvaro’s Zoomcamp notes
• Alvaro’s coach
• The importance of mathematical knowledge to a transition into ML
• Preparing for technical interviews
• Alvaro’s typical workday
• Alvaro’s team’s tech stack
• The importance of a technical background to transitioning into ML

Links:

• Alvaro's CV: https://www.dropbox.com/s/89hkt3ug0toqa2n/CV%20nou%20-%20angl%C3%A8s.pdf?dl=0
• Github profile: https://github.com/ziritrion
• LinkedIn profile: https://www.linkedin.com/in/alvaronavas/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcampJoin 

DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Supreet’s background
• Responsible AI
• Example of explainable AI
• Responsible AI vs explainable AI
• Explainable AI tools and frameworks (glass box approach)
• Checking for bias in data and handling personal data
• Understanding whether your company needs certain type of data
• Data quality checks and automation
• Responsibility vs profitability
• The human touch in AI
• The trade-off between model complexity and explainability
• Is completely automated AI out of the question?
• Detecting model drift and overfitting
• How Supreet became interested in explainable AI
• Trustworthy AI
• Reliability vs fairness
• Bias indicators
• The future of explainable AI
• About DataBuzz
• The diversity of data science roles
• Ethics in data science
• Conclusion

Links:

• LinkedIn: https://www.linkedin.com/in/supreet-kaur1995/
• Databuzz page: https://www.linkedin.com/company/databuzz-club/
• Medium Blog Page: https://medium.com/@supreetkaur_66831

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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Have a great weekend!

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We talked about:

• David’s background
• A day in the life of a professor
• David’s current projects
• Starting a school
• The different types of professors
• David’s recent papers
• Similarities and differences between research labs and startups
• Finding (or creating) good datasets
• David’s lab
• Balancing research and teaching as a professor
• David’s most rewarding research project
• David’s most underrated research project
• David’s virtual data science seminars on YouTube
• Teaching at universities without doing research
• Staying up-to-date in research
• David’s favorite conferences
• Selecting topics for research
• Convincing students to stay in academia and competing with industry
• Finding David online

Links: 

• David A. Bader: https://davidbader.net/
• NJIT Institute for Data Science: https://datascience.njit.edu/
• Arkouda: https://github.com/Bears-R-Us/arkouda
• NJIT Data Science YouTube Channel: https://www.youtube.com/c/NJITInstituteforDataScience

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Christiaan’s background
• Usual ways of collecting and curating data
• Getting the buy-in from experts and executives
• Starting an annotation booklet
• Pre-labeling
• Dataset collection
• Human level baseline and feedback
• Using the annotation booklet to boost annotation productivity
• Putting yourself in the shoes of annotators (and measuring performance)
• Active learning
• Distance supervision
• Weak labeling
• Dataset collection in career positioning and project portfolios
• IPython widgets
• GDPR compliance and non-English NLP
• Finding Christiaan online

Links:

• My personal blog: https://useml.net/
• Comtura, my company: https://comtura.ai/
• LI: https://www.linkedin.com/in/christiaan-swart-51a68967/
• Twitter: https://twitter.com/swartchris8/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Zhamak’s background
• What is Data Mesh?
• Domain ownership
• Determining what to optimize for with Data Mesh
• Decentralization
• Data as a product
• Self-serve data platforms
• Data governance
• Understanding Data Mesh
• Adopting Data Mesh
• Resources on implementing Data Mesh

Links:

• Free 30-day code from O'Reilly: https://learning.oreilly.com/get-learning/?code=DATATALKS22
• Data Mesh book: https://learning.oreilly.com/library/view/data-mesh/9781492092384/
• LinkedIn: https://www.linkedin.com/in/zhamak-dehghani

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Mehdi’s background
• The difference between startup, scale-up and enterprise
• Hypergrowth
• Data platform engineers in a scale-up environment
• What a data platform is and who builds it
• Managing the fast pace of a scale-up while ensuring personal growth
• Should a senior data person consider a scale-up or an enterprise?
• Should a junior data person consider a scale-up or an enterprise?
• Sourcing talent for hyper-growth companies and developing a community culture
• Generating content and getting feedback
• Generalization vs specialization for data engineers in a scale-up
• The ratio of work between platform building and use case pipelines
• Being proactive in order to progress to mid or senior level
• Caps and bass guitars
• MehdiO DataTV and DataCreators.Club (Mehdi’s YouTube Channel and podcast)

Links:

• Mehdi's YouTube channel: https://www.youtube.com/channel/UCiZxJB0xWfPBE2omVZeWPpQ
• Mehdi's Linkedin: https://linkedin.com/in/mehd-io/
• Mehdi's Medium Blog: https://medium.com/@mehdio
• Mehdi's data creators club: https://datacreators.club/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Alexander’s background
• The role of Partner at Königsweg
• Being part of the data and AI community
• How Alexander became chair at PyData
• Alexander’s many talks and advice on giving them
• Explaining AI to managers
• Why being able to explain machine learning to managers is important
• The experimentational nature of AI and why it’s not a cure-all
• Innovation requires patience
• Convincing managers not to use AI or ML when there are better (simpler) solutions
• The role of MLOps in enterprises
• Thinking about the mid- and long-term when considering solutions
• Finding Alexander online

Links: 

• Alexander's Twitter: https://twitter.com/hendorf
• Alexander's LinkedIn: https://www.linkedin.com/in/hendorf/
• Königsweg: https://www.koenigsweg.com
• PyData Südwest: https://www.meetup.com/pydata-suedwest/
• PyData Frankfurt: https://www.meetup.com/pydata-frankfurt/
• PyConDE & PyData Berlin: https://pycon.de

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Danny’s background
• What an MLOps Architect does
• The popularity of MLOps Architect as a role
• Convincing an employer that you can wear many different hats
• Interviewing for the role of an MLOps Architect
• How Danny prioritizes work with data scientists
• Coming to WhyLabs when you’ve already got something in production vs nothing in production
• Market awareness regarding the importance of model monitoring
• How Danny (WhyLabs) chooses tools
• ONNX
• Common trends in tooling setups
• The most rewarding thing for Danny in ML and data science
• Danny’s secret for staying sane while wearing so many different hats
• T-shaped specialist, E-shaped specialist, and the horizontal line
• The importance of background for the role of an MLOps Architect
• Key differences for WhyLogs free vs paid
• Conclusion and where to find Danny online

Links:

• Matt Turck: https://mattturck.com/data2021/
• AI Observability Platform: https://whylabs.ai/observability
• Danny's LinkedIn: https://www.linkedin.com/in/dleybz/
• Whylabs' website: https://whylabs.ai/
• AI Infrastructure Alliance: https://ai-infrastructure.org/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• DataTalks.Club intro
• Tereza’s background
• Working as a coach
• Identifying the mismatches between your needs and that of a company
• How to avoid misalignments
• Considering what’s mentioned in the job description, what isn’t, and why
• Diversity and culture of a company
• Lack of a salary in the job description
• Way of doing research about the company where you will potentially work
• How to avoid a mismatch with a company other than learning from your mistakes
• Before data, during data, after data (a company’s data maturity level)
• The company’s tech stack
• Finding Tereza online

Links: 

• Decoding Data Science Job Descriptions (talk): https://www.youtube.com/watch?v=WAs9vSNTza8
• Talk at ConnectForward: https://www.youtube.com/watch?v=WAs9vSNTza8
• Slides: https://www.slideshare.net/terezaif/decoding-data-science-job-descriptions-250687704
• Talk at DataLift: https://www.youtube.com/watch?v=pCtQ0szJiLA
• Slides: https://www.slideshare.net/terezaif/lessons-learned-from-hiring-and-retaining-data-practitioners

MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

Links:

• Explore some Data Science for Social Good projects: http://www.dssgfellowship.org/projects/
• Bi-weekly Ethics in AI Coffee Chat: https://www.meetup.com/coffee-ethics-ai/
• Make a Social Impact with your Job: https://tinyurl.com/80khours
• Course in Data Ethics: https://ethics.fast.ai/
• Data Science for Social Good Berlin: https://dssg-berlin.org/
• CorrelAid: https://correlaid.org/
• DataKind: https://www.datakind.org/
• Christine's LinkedIn: https://www.linkedin.com/in/christinecepelak/
• Christine's Twitter: https://twitter.com/CLcep

MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Olga’s career journey
• Hiring data scientists now vs 7 years ago
• The two qualities of an excellent data scientist
• What makes Alexey do this podcast
• How Alexey get the latest information on data science
• How Olga checks a candidate’s technical skills
• How to make an answer stand out (showing your depth of knowledge)
• A strong mathematical background vs a strong engineering background
• When Auto ML will replace the need to have data scientists
• Should data scientists transition into management? (the importance of communication in an organization)
• Switching from a data analyst role to a data scientist
• Attracting female talent in data science
• Changing a job description to find talent
• Long gaps in the CV
• Eierlegende Wollmilchsau

Links:

• Olga's LinkedIn: https://www.linkedin.com/in/olgaivina/
• Olga's Twitter: https://twitter.com/olgaivina

MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about: 

• Will’s background
• Will’s open source projects
• S3Fs and PyFile systems
• Inspiration for open source projects
• Will as a freelancer
• Starting a company from a tweet (Rich and Textual)
• Building in public (Will’s approach to social media)
• The workforce and roadmap of Textualize.io
• The importance of working on open source for Textualize employees
• The workflow of and contributions to Textualize
• Getting your first thousand GitHub Stars (going viral)
• Suggestions for those who wish to start in the open-source space
• Finding Will online

Links: 

• Twitter: https://twitter.com/willmcgugan
• Textualize website: https://www.textualize.io/
• Textualize GitHub: https://github.com/textualize

MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Lisa’s background
• Centralized org vs decentralized org
• Hybrid org (centralized/decentralized)
• Reporting your results in a data organization
• Planning in a data organization
• Having all the moving parts work towards the same goals
• Which approach Twitter follows (centralized vs decentralized)
• Pros and cons of a decentralized approach
• Pros and cons of a centralized approach
• Finding a common language with all the functions of an org
• Finding the right approach for companies that want to implement data science
• How many data scientists does a company need?
• Who do data scientists report huge findings to?
• The importance of partnering closely with other functions of the org
• The role of Product Managers in the org and across functions
• Who does analytics at Twitter (analysts vs data scientists)
• The importance of goals, objectives and key results
• Conflicting objectives
• The importance of research
• Finding Lisa online

Links:

• LinkedIn: https://www.linkedin.com/in/cohenlisa/
• Twitter: https://twitter.com/lisafeig
• Medium: https://medium.com/@lisa_cohen
• Lisa Cohen's YouTube videos: https://www.youtube.com/playlist?list=PLRhmnnfr2bX7-GAPHzvfUeIEt2iYCbI3w

MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Merve’s background
• Merve’s first contributions to open source
• What Merve currently does at Hugging Face (Hub, Spaces)
• What is means to be a developer advocacy engineer at Hugging Face
• The best way to get open source experience (Google Summer of Code, Hacktoberfest, and sprints)
• The peculiarities of hiring as it relates to code contributions
• Best resources to learn about NLP besides Hugging Face
• Good first projects for NLP
• The most important topics in NLP right now
• NLP ML Engineer vs NLP Data Scientist
• Project recommendations and other advice to catch the eye of recruiters
• Merve on Twitch and her podcast
• Finding Merve online
• Merve and Mario Kart

Links:

• Hugging Face Course: https://hf.co/course
• Natural Language Processing in TensorFlow: https://www.coursera.org/learn/natural-language-processing-tensorflow
• Github ML Poetry: https://github.com/merveenoyan/ML-poetry
• Tackling multiple tasks with a single visual language model: https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model
• Hugging Face big science/TOpp: https://huggingface.co/bigscience/T0pp
• Pathways Language Model (PaLM) blog: https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html

MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Misra’s background
• What data scientists do
• Consultant data scientists vs in-house data scientists (and freelancers)
• Expectations for data scientists
• The importance of keeping up to date with AI developments (FOMA)
• How does DALL·E 2 work and should you care?
• Going to conferences to stay up to date
• The most pressing issue for data scientists
• Fighting FOMA and imposter syndrome
• Knowing when you have enough knowledge of a framework
• The “best” type of data scientist
• Being a generalist vs a specialist
• Advice for entry-level data entering an oversaturated market
• Catching the eye of big AI companies
• Choosing a project for your portfolio
• The importance of having a Ph.D. or Master’s degree in data science
• Finding Misra online

Links:

• Mısra's YouTube channel: https://www.youtube.com/channel/UCpNUYWW0kiqyh0j5Qy3aU7w
• Twitter: https://twitter.com/misraturp
• Hands-on Data Science: Complete Your First Portfolio Project: https://www.soyouwanttobeadatascientist.com/hods

MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.htm

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We talked about:

• Adrian’s background
• Freelancing vs Employment
• Risk and occupancy rate in freelancing
• The scariest part of freelancing
• Adrian’s first projects
• Freelancing 5 years later
• Pay rates in freelancing
• Acquiring skills while freelancing
• Working with recruitment agencies and networking
• Looking for projects and getting clients
• Freelancing vs consulting
• Clarity in clients’ expectations (scope of work)
• Building your network
• Freelancing platforms
• Adrian’s data loading prototype
• Going from freelancing to making your own product (and other investments)
• The usefulness of a portfolio
• Introverts in freelancing
• Is it possible to work for 3 months a year in freelancing?
• Choosing projects and skill-building strategy (focusing on interests)
• Freelancing in Berlin
• Clients’ expectations for freelancers vs employees
• Working with more than one client at the same time
• Adrian’s freelance cooperative on Slack
• Other advice for novice freelancers (networking)
• Finding Adrian online

Links:

• Github: https://github.com/scale-vector
• Slack Community: https://join.slack.com/t/berlindatacol-szn7050/shared_invite/zt-19dp8msp0-pP4Av3_fVFBbsdrzPROEAg

MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Summary of “Getting a Data Engineering Job” webinar
• Python and engineering skills
• Interview process
• Behavioral interviews
• Technical interviews
• Learning Python and SQL from scratch
• Is having non-coding experience a disadvantage?
• Analyst or engineer?
• Do you need certificates?
• Do I need a master’s degree?
• Fully remote data engineering jobs
• Should I include teaching on my resume?
• Object-oriented programming for data engineering
• Python vs Java/Scala
• SQL and Python technical interview questions
• GCP certificates
• Is commercial experience really necessary?
• From sales to engineering
• Solution engineers
• Wrapping up

Links:

• Getting a Data Engineering Job (webinar): https://www.youtube.com/watch?v=yvEWG-S1F_M
• The Flask Mega-Tutorial Part I - Hello, World! blog: https://blog.miguelgrinberg.com/post/the-flask-mega-tutorial-part-i-hello-world
• Mode SQL Tutorial: https://mode.com/sql-tutorial/

MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Daynan’s background
• Astronomy vs cosmology
• Applications of data science and machine learning in astronomy
• Determining signal vs noise
• What the data looks like in astronomy
• Determining the features of an object in space
• Ground truth for space objects
• Why water is an important resource in the space economy
• Other useful resources that can be found in asteroids
• Sources of asteroids
• The data team at an asteroid mining company
• Open datasets for hobbyists
• Mission and hardware design for asteroid mining
• Partnerships and hires

Links:

• LinkedIn: https://www.linkedin.com/in/daynan/
• We're looking for a Sr Data Engineer: https://boards.eu.greenhouse.io/karmanplus/jobs/4027128101?gh_jid=4027128101
• Minor Planet Center: https://minorplanetcenter.net/- JPL Horizons has a nice set of APIs for accessing data related to small bodies (including asteroids): https://ssd.jpl.nasa.gov/api.html
• ESA has NEODyS: https://newton.spacedys.com/neodys
• IRSA catalog that contains image and catalog data related to the WISE/NEOWISE data (and other infrared platforms): https://irsa.ipac.caltech.edu/frontpage/
• NASA also has an archive of data collected from their various missions, including a node related to small bodies: https://pds-smallbodies.astro.umd.edu/
• Sub-node directly related to asteroids: https://sbn.psi.edu/pds/
• Size, Mass, and Density of Asteroids (SiMDA) is a nice catalog of observed asteroid attributes (and an indication of how small our sample size is!): https://astro.kretlow.de/?SiMDA
• The source survey data, several are useful for asteroids: Pan-STARRS (https://outerspace.stsci.edu/display/PANSTARRS)

MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Juan’s background
• Typical problems in marketing that are solved with ML
• Attribution model
• Media Mix Model – detecting uplift and channel saturation
• Changes to privacy regulations and its effect on user tracking
• User retention and churn prevention
• A/B testing to detect uplift
• Statistical approach vs machine learning (setting a benchmark)
• Does retraining MMM models often improve efficiency?
• Attribution model baselines
• Choosing a decay rate for channels (Bayesian linear regression)
• Learning resource suggestions
• Bayesian approach vs Frequentist approach
• Suggestions for creating a marketing department
• Most challenging problems in marketing
• The importance of knowing marketing domain knowledge for data scientists
• Juan’s blog and other learning resources
• Finding Juan online

Links:

• Juan's PyData talk on uplift modeling: https://youtube.com/watch?v=VWjsi-5yc3w
• Juan's website: https://juanitorduz.github.io
• Introduction to Algorithmic Marketing book: https://algorithmic-marketing.online
• Preventing churn like a bandit: https://www.youtube.com/watch?v=n1uqeBNUlRM

MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about: 

• Gloria’s background
• Working with MATLAB, R, C, Python, and SQL
• Working at ICE
• Job hunting after the bootcamp
• Data engineering vs Data science
• Using Docker
• Keeping track of job applications, employers and questions
• Challenges during the job search and transition
• Concerns over data privacy
• Challenges with salary negotiation
• The importance of career coaching and support
• Skills learned at Spiced
• Retrospective on Gloria’s transition to data and advice
• Top skills that helped Gloria get the job
• Thoughts on cloud platforms
• Thoughts on bootcamps and courses
• Spiced graduation project
• Standing out in a sea of applicants
• The cohorts at Spiced
• Conclusion

Links:

• LinkedIn: https://www.linkedin.com/in/gloria-quiceno/
• Github: https://github.com/gdq12

MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Jeff’s background
• Getting feedback to become a better teacher
• Going from engineering to teaching
• Jeff on becoming a curriculum writer
• Creating a curriculum that reinforces learning
• Jeff on starting his own data engineering bootcamp
• Shifting from teaching ML and data science to teaching data engineering
• Making sure that students get hired
• Screening bootcamp applicants
• Knowing when it’s time to apply for jobs
• The curriculum of JigsawLabs.io
• The market demand of Spark, Kafka, and Kubernetes (or lack thereof)
• Advice for data analysts that want to move into data engineering
• The market demand of ETL/ELT and DBT (or lack thereof)
• The importance of Python, SQL, and data modeling for data engineering roles
• Interview expectations
• How to get started in teaching
• The challenges of being a one-person company
• Teaching fundamentals vs the “shiny new stuff”
• JigsawLabs.io
• Finding Jeff online

Links:

• Jigsaw Labs: https://www.jigsawlabs.io/free
• Teaching my mom to code: https://www.youtube.com/watch?v=OfWwfTXGjBM
• Getting a Data Engineering Job Webinar with Jeff Katz: https://www.eventbrite.de/e/getting-a-data-engineering-job-tickets-310270877547

MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Jessica’s background
• Giving a talk at a tech conference about coffee
• Jessica’s transition into tech (How to get started)
• Going from learning to actually making money
• Landing your first job in tech
• Does your age matter when you’re trying to get a job?
• Challenges that Jessica faced in the beginning of her career
• Jessica’s role at PyLadies
• Fighting the Imposter Syndrome
• Generational differences in digital literacy and how to improve it
• Events organized by PyLadies
• Jessica’s beginnings at PyLadies (organizing events)
• Jessica’s experience with public speaking
• The impact of public speaking on your career
• Tips for public speaking
• Jessica’s work at Ecosia
• Discrimination in the tech industry (and in general)
• Finding Jessica online

Links:

• Ecosia's website: https://www.ecosia.org/
• Ecosia's blog: https://blog.ecosia.org/ecosia-financial-reports-tree-planting-receipts/
• PyLadies Berlin: https://berlin.pyladies.com/
• PyLadies' Meetup: https://meetup.com/PyLadies-Berlin
• Code Academy: https://www.codecademy.com/
• Freecodecamp: https://www.freecodecamp.org/
• Coursera Machine Learning: https://www.coursera.org/learn/machine-learning
• ML Bookcamp code: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp
• Google Summer code: https://summerofcode.withgoogle.com/
• Outreachy website: https://www.outreachy.org/
• Alumni Interview: https://railsgirlssummerofcode.org/blog/2020-03-17-alumni-interview-jessica
• Python pizza: https://python.pizza/
• Pycon: https://pycon.it/en
• Pycon 2022: https://2022.pycon.de/

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Nicolas’ background
• The tech talent market in different countries
• Hiring data scientists vs data engineers
• A spike in interest for data engineering roles
• The importance of recruiters having technical knowledge
• The main challenges of hiring data engineers
• The difference in hiring junior, mid, and senior level data engineers
• Things recruiters look for in people who switch to a data engineering role
• The importance of knowing cloud tools
• The importance of knowing infrastructure tools
• Preparing for the interview
• The importance of a formal education
• The importance having a project portfolio
• How your current domain influence the interview
• Conclusion

Links:

• Nicolas' Twitter: https://twitter.com/n_rassam
• Nicolas' LinkedIn: https://www.linkedin.com/in/nicolasrassam/
• Onfido is hiring: https://onfido.com/engineering-technology/
• Interview with Alicja about recruiting data scientists: https://datatalks.club/podcast/s07e02-recruiting-data-professionals.html
• Webinar "Getting a Data Engineering Job" with Jeff Katz: https://eventbrite.com/e/310270877547

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Christopher’s background
• The essence of DataOps
• Also known as Agile Analytics Operations or DevOps for Data Science
• Defining processes and automating them (defining “done” and “good”)
• The balance between heroism and fear (avoiding deferred value)
• The Lean approach
• Avoiding silos
• The 7 steps to DataOps
• Wanting to become replaceable
• DataOps is doable
• Testing tools
• DataOps vs MLOps
• The Head Chef at Data Kitchen
• What’s grilling at Data Kitchen?
• The DataOps Cookbook

Links:

• DataOps Manifesto website: https://dataopsmanifesto.org/en/
• DataOps Cookbook: https://dataops.datakitchen.io/pf-cookbook
• Recipes for DataOps Success: https://dataops.datakitchen.io/pf-recipes-for-dataops-success
• DataOps Certification Course: https://info.datakitchen.io/training-certification-dataops-fundamentals
• DataOps Blog: https://datakitchen.io/blog/
• DataOps Maturity Model: https://datakitchen.io/dataops-maturity-model/
• DataOps Webinars: https://datakitchen.io/webinars/

Join DataTalks.Club: https://datatalks.club/slack.html  

Our events: https://datatalks.club/events.html

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We talked about:

• Stefan’s background
• Applications of machine learning in healthcare
• Sidekick Health – gamified therapeutics
• How is working for King different from Sidekick Health?
• The rewards systems in gamified apps
• The importance of building a strong foundation for a data science team
• The challenges of building an app in the healthcare industry
• Dealing with ethics issues
• Sidekick Health’s personalized recommendations and content
• The importance of having the right approach in A/B tests (strong analytics and good data)
• The importance of having domain knowledge to work as a data professional in the healthcare industry
• Making a data-driven company
• Risks for Sidekick Health
• Sidekick Health growth strategy
• Using AI to help people live better lives

Links:

• LinkedIn: https://www.linkedin.com/in/stefanfreyrgudmundsson/
• Job listings: https://sidekickhealth.bamboohr.com/jobs/

Join DataTalks.Club: https://datatalks.club/slack.html

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We talked about:

• Liesbeth’s background
• What is design?
• The importance of interaction in design
• Design as a process (Double Diamond technique)
• How long does it take to go from an idea to finishing the second diamond?
• Design thinking (Google’s PAIR)
• What is a Design Sprint and who should participate in it?
• Why should data specialists care about design?
• Challenging your task-giver (asking “why”)
• How to avoid the “Chinese whisper game” (reiterating the problem)
• Defining the roadmap for data science teams
• What is innovation?
• Bringing innovation to your management
• Task force-team approach to solving problems
• Innovation, resource management issues, and using data to back your ideas
• Words of advice for those interested in design and innovation

Links:

• LinkedIn: https://www.linkedin.com/in/liesbeth-dingemans/
• Medium posts on design, innovation, art and AI: https://medium.com/@liesbethmd

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We talked about:

• Marijn’s background
• Standing out in data science
• Doing the opposite of what people tell you
• Don’t shoot the messenger (carefully sharing your findings)
• Advising the seniors
• Bite off more than you can chew, then chew
• Marijn’s side projects (finding value in doing things you find interesting)
• Building a project portfolio
• Marijn’s NGO project
• The importance of a team
• Open source intelligence (OSINT)
• The importance of soft skills for data experts
• Marijn’s LinkedIn growth strategy and tips

Links:  

• Twitter: https://twitter.com/MarijnMarkus
• LinkedIn: https://www.linkedin.com/in/marijnmarkus/

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We talked about:

• kDimensions
• Being self-employed
• Visual engineering
• Constrain yourself to get creative
• Coming up with ideas
• Visualising difficult concepts
• The process of creating visuals
• Creating visuals
• Learning to create visuals for engineers
• Consuming with intention to create
• Learning by breaking code
• Earning with visuals
• Adding visuals to blog posts
• Meor’s book: visual introduction to deep learning

Links:  

• A Visual Introduction to Deep Learning by Meor Amer: https://gumroad.com/a/63231091
• kDimensions website: https://kdimensions.com/
• Book to learn about Figma: https://figmabook.com/
• Jack Butcher's approach: https://www.youtube.com/watch?v=azhqc4K-GAE

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We talked about:

• Juan Pablo's Backround
• Data engineering resources
• Teaching calculus
• Transitioning to Analytics
• Data Analytics bootcamp
• Getting money while studying
• Going to meetups to get a job
• Looking for uncrowded doors
• Using LinkedIn
• Portfolio
• Talking to people on meetups
• Eight tips to get your first analytics job
• Consider contracts and temporary roles
• Getting experience with non-profits
• Create your own internship
• Networking
• Website for hosting a portfolio
• I’m a math teacher. What should I learn first?
• Analytics engineering
• Best suggestion: keep showing up
• Networking on online conferences
• Communication skills and being organized

Links:

• Website: https://www.thatjuanpablo.com/
• Twitter: https://twitter.com/thatjuanpablo
• BROKE teacher to FAANG engineer Twitter thread: https://twitter.com/thatjuanpablo/status/1475806246317875203
• LinkedIn: https://www.linkedin.com/in/thatjuanpablo/

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We talked about:

• Ellen’s background
• Why Ellen switched from data science to data engineering
• The overlap between data science and data engineering
• Skills to learn and improve for data engineering
• Ways to pick up and improve skills (advice for making the transition)
• What makes a data engineering course “good”
• Languages to know for data engineering
• The easiest part of transitioning into data engineering
• The hardest part of transitioning into data engineering
• Common data engineering team distributions
• People who are both data scientists and data engineers
• Pet projects and other ways to pick up development skills
• Dealing with cloud processing costs (alerts, billing reports, trial periods)
• Advice for getting into entry level positions
• Which cloud platform should data engineers learn?

Links:

• Twitter: https://twitter.com/ellen_koenig
• LinkedIn: https://www.linkedin.com/in/ellenkoenig/

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We talked about:

• Rahul’s background
• What do data engineering managers do and why do we need them?
• Balancing engineering and management
• Rahul’s transition into data engineering management
• The importance of updating your skill set
• Planning the transition to manager and other challenges
• Setting expectations for the team and measuring success
• Data reconciliation
• GDPR compliance
• Data modeling for Big Data
• Advice for people transitioning into data engineering management
• Staying on top of trends and enabling team members
• The qualities of a good data engineering team
• The qualities of a good data engineer candidate (interview advice)
• The difference between having knowledge and stuffing a CV with buzzwords
• Advice for students and fresh graduates
• An overview of an end-to-end data engineering process

Links:

• Rahul's LinkedIn: https://www.linkedin.com/in/16rahuljain/

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We talked about:

• Jakob’s background
• The importance of A/B tests
• Statistical noise
• A/B test example
• A/B tests vs expert opinion
• Traffic splitting, A/A tests, and designing experiments
• Noisy vs stable metrics – test duration and business cycles
• Z-tests, T-tests, and time series
• A/B test crash course advice
• Frequentist approach vs Bayesian approach
• A/B/C/D tests
• Pizza dough

Links:

• Jakob's LinkedIn: https://www.linkedin.com/in/jakob-graff-a6113a3a/
• Product Analyst role at Inkitt: https://jobs.lever.co/inkitt/d2b0427a-f37f-4002-975d-28bd60b56d70

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We talked about:

• Valerii’s background
• Who goes through an ML system design interview
• System design VS ML System design
• Preparing for ML system design interviews
• Machine learning project checklist
• The importance of defining a goal and ways of measuring it
• What to do after you set a goal
• Typical components of an ML system
• Applying ML systems to real-world problems
• System design and coding in interviews for new graduates
• Humans in the validation of model performance

Links:

• Valerii's telegram channel (in Russian): t.me/cryptovalerii

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We talked about:

• Lindsay’s background
• Spiced Academy
• Career coaching role
• Reframing your experience
• Helping with career problems
• Finding what interests you
• Tailoring a CV and “spray and pray”
• Career coaching outside a bootcamp
• Imposter syndrome
• After bootcamp
• Internships
• Working with recruiters
• Networking on LinkedIn

Links:

• Lindsay's LinkedIn: https://www.linkedin.com/in/lindsay-mcquade/
• Impostor questionnaire: http://impostortest.nickol.as/

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We talked about:

• Greg’s background
• Responsibilities of Data Product Manager
• Understanding customer journey
• Interviewing business partners and decision-makers
• Products sense, product mindset, and product roadmap
• Working backwards
• Driving the roadmap
• Building a roadmap in Excel
• Measuring success
• Advice for teams that don’t have a product manager

Links:

• Greg's LinkedIn: https://www.linkedin.com/in/greg-coquillo/

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We talked about:

• Alicja’s background
• The hiring process
• Sourcing and recruiting
• Managing expectations
• Making the job description attractive
• Selecting profiles during sourcing
• Profile keywords
• The importance of a Master’s vs a Bachelor’s degree vs a PhD
• Improving CV
• Interview with the recruiter
• Salary expectations
• Advice for “career changers”
• Cover letters
• Data analysts
• Double Bachelor’s degrees
• The most difficult part of hiring
• Coursera courses on the CV
• Making a good impression on recruiters

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We talked about:

• Alexey’s background
• Being a principal data scientist
• DataTalks.Club
• The beginning and growth of DataTalks.Club
• Sustaining the pace
• Types of talks
• Popular and favorite talks
• Making DataTalks.Club self-sufficient
• Alexey’s book and course
• Advice for people starting in data science and staying motivated
• Not keeping up to date with new tools
• Staying productive
• Learning technical subjects and keeping notes
• Inspiration and idea generation for DataTalks.Club

Links:

• https://eugeneyan.com/writing/informal-mentors-alexey-grigorev/

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We don't have a new episode this week, but we have an amazing conversation with Sejal Vaidya from August

We talked about

• Sejal's background
• Why transitioning to ML engineering
• Three phases of development of a project
• Why data engineers should get involved in ML
• Technologies
• Tips for people who want to transition
• Soft skills and understanding requirements
• Helpful resources

Resources:

• ML checklist (https://twolodzko.github.io/ml-checklist.html)
• Machine Learning Bookcamp (https://mlbookcamp.com/)
• Made with ML course (https://madewithml.com)
• Full-stack deep learning (https://fullstackdeeplearning.com)
• Newsletters: mlinproduction, huyenchip.com, jeremyjordan.me, mihaileric.com
• Sejal's "Production ML" twitter list (https://twitter.com/i/lists/1212819218959351809)

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We talked about:

• Mariano’s background
• Typical day of a manager
• Becoming a manager
• Preparing for the transition
• Balancing projects and assumptions
• Search and recommendations
• Dealing with unfamiliar domains
• Structuring projects
• Connecting product and data science
• Rules of Machine Learning
• CRISP-DM and deployment
• Giving feedback
• Dealing with people leaving the team
• Doing technical work as a manager
• Dealing with bad hires
• Keeping up with the industry

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We talked about:

• Ivan’s role at Personio
• Ivan’s background
• Studying technical management
• Managing a software team
• NLP teams
• NLP engineers
• Becoming an NLP engineer
• Computer vision
• NLP engineer vs ML engineer
• Conversational designers
• Linguistics outside of chatbots
• When does a team need an NLP engineer or a linguist?
• The future of NLP
• NLP pipelines
• GPT-3
• Problems of GPT-3
• Does GPT-3 make everything obsolete?
• What NLP actually is?
• Does NLP solve problems better than humans?
• State of language translation
• NLP Pandect

Links:

• https://github.com/ivan-bilan/The-NLP-Pandect
• https://github.com/ivan-bilan/The-Engineering-Manager-Pandect
• https://github.com/ivan-bilan/The-Microservices-Pandect
• Ivan's presentation about NLP: https://www.youtube.com/watch?v=VRur3xey31s

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We talked about

• Geo’s background
• Technical Product Manager
• Building ML platform
• Working on internal projects
• Prioritizing the backlog
• Defining the problems
• Observability metrics
• Avoiding jumping into “solution mode”
• Breaking down the problem
• Important skills for product managers
• The importance of a technical background
• Data Lead vs Staff Data Scientist vs Data PM
• Approvals and rollout
• Engineering/platform teams
• Data scientists’ role in the engineering team
• Scrum and Agile in data science
• Transitioning from Data Scientist to Technical PM
• Books to read for the transition
• Transitioning for non-technical people
• Doing user research
• Quality assurance in ML
• Advice for supporting an ML team as a Scrum master

Links:

• Geo's LinkedIn: https://www.linkedin.com/in/geojolly/
• Product School community: https://productschool.com/
• http://theleanstartup.com/
• Netflix CPO Medium blog: https://gibsonbiddle.medium.com/
• Glovo is hiring: https://jobs.glovoapp.com/en/?d=4040726002

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We talked about:

• CJ’s background
• Evolutionary biology
• Learning machine learning
• Learning on the job and being honest with what you don’t know
• Convincing that you will be useful
• CJ’s first interview
• Transitioning to industry
• Tailoring your CV
• Data science courses
• Moving to Berlin
• Being selective vs ‘spray and pray’
• Moving on to new jobs
• Plan for transitioning to industry
• Requirements for getting hired
• Publications, portfolios and pet projects
• Adjusting to industry
• Bad habits from academia
• Topics with long-term value
• CJ’s textbook

Links:

• CJ's LinkedIn: https://www.linkedin.com/in/christina-jenkins/
• Positions for master students: one two

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We talked about:

• Eleni’s background
• Spatial data analytics
• Responsibilities of a postdoc
• Publishing papers
• Best places for data management papers
• Differences between postdoc and PhD
• Helping students become successful
• Research at the DIMA group
• Identifying important research directions
• Reviewing papers
• Underrated topics in data management
• Research in data cleaning
• Collaborating with others
• Choosing the field for Master’s students
• Choosing the topic for a Master thesis
• Should I do a PhD?
• Promoting computer science to female students

Links:

• https://www.user.tu-berlin.de/tzirita/

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We talked about:

• Sara’s background
• Product designer’s responsibilities
• Data product manager’s responsibilities
• Planning with the team
• Design thinking and product design
• Data PMs vs regular PMs
• Skill requirements for Data PMs
• Going from a product designer to a data product manager
• Case studies
• Resources for learning about product management
• Data PM’s biggest challenge
• Multitasking and context switching
• Insights from user interviews
• Using new, unfamiliar tools
• Documentation
• Idea generation
• Do Data PMs need to know ML?

Links:

• Product Management Courses: https://www.lennyrachitsky.com/course and https://www.reforge.com/mastering-product-management
• Product Management Reading:
• https://svpg.com/inspired-how-to-create-products-customers-love/ and https://steveblank.com/category/customer-development/
• Data Engineering for Noobs: https://www.datacamp.com/

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We talked about:

• Barbara’s background
• Do you need a manager or an expert?
• Technical and non-technical requirements for managers
• Importance of technical skills for managers
• Responsibilities and skills of a manager
• Importance of technical background for managers
• Getting involved in business development and sales
• Developing the team
• Checking team’s work
• Data science expert
• Hiring experts
• Who should we hire first?
• Can an expert build a team?
• Data science managers in startups
• Project management
• Ensuring that projects provide value
• Questions before starting a project
• Women in data science
• Finding Barbara online
• General advice

Link:

• Barbara's LinkedIn: https://www.linkedin.com/in/barbara-sobkowiak-1a4a9568

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We talked about:

• Nick’s background
• Being a career coach
• Overview of the hiring process
• Behavioral interviews for data scientists
• Preparing for behavioral interviews
• Handling "tricky" questions
• Project deep dive
• Business context
• Pacing, rambling, and honesty
• “What’s your favorite model?”
• What if I haven’t worked on a project that brought $1 mln?
• Different questions for different levels
• Product-sense interviews
• Identifying key metrics in unfamiliar domains
• Tech blogs
• Cold emailing

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We talked about:

• Noah’s background
• Solopreneurship
• A day of a solopreneur
• Exponential vs linear work
• Escaping the office work - digging the tunnel
• Structuring goals
• Staying motivated
• Publishing books
• Planning out books
• Writing a book is like preparing to run a marathon
• Distributed income
• Getting started as a solopreneur
• Lowering expenses and adding time
• The right time to quit full-time
• Building a network
• Teaching at universities

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Links:

• https://join.slack.com/t/integratedmlai/shared_invite/zt-r3hpj44k-gfhf1pzIt3jixrATyXCWnQ
• https://www.linkedin.com/in/thomives/

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We talked about:

• Caitlin’s background
• The last mile in data
• The Pareto Principle
• Failing to use data
• Making sure data is used
• Communicating with decision-makers
• Working backwards from the last mile
• Understanding how data drives decisions
• Sketching and prototyping
• Showing the benefits of power data
• Measurability
• Driving change in data
• Asking high-leverage questions
• Resistance from users
• Understanding domain experts
• Linear projects vs circular projects
• Recommendations for data analyst students
• Finding Caitlin online

Links:

• Emelie's talk
• https://locallyoptimistic.com/post/linear-and-circular-projects-part-1/
• https://locallyoptimistic.com/post/linear-and-circular-projects-part-2/

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We talked about:

• Rishabh's background
• Rishabh’s experience as a sales engineer
• Prescriptive analytics vs predictive analytics
• The problem with the term ‘data science’
• Is machine learning a part of analytics?
• Day-to-day of people that work with ML
• Rule-based systems to machine learning
• The role of analysts in rule-based systems and in data teams
• Do data analysts know data better than data scientists?
• Data analysts’ documentation and recommendations
• Iterative work - data scientists/ML vs data analysts
• Analyzing results of experiments
• Overlaps between machine learning and analytics
• Using tools to bridge the gap between ML and analytics
• Do companies overinvest in ML and underinvest in analystics?
• Do companies hire data scientists while forgetting to hire data analysts?
• The difficulty of finding senior data analysts
• Is data science sexier than data analytics?
• Should ML and data analytics teams work together or independently?
• Building data teams
• Rishabh’s newsletter – MLOpsRoundup

Links:

• https://mlopsroundup.substack.com/
• https://twitter.com/rish_bhargava

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We talked about:

• Tammy’s background
• Being the chief of data
• First projects as the first data person in a company
• Initial resistance
• Expanding the team
• Role of business analyst
• Platanomelon’s stack
• Order for growing the data team
• Demand forecasting
• Should analysts know machine learning
• Qualifications for the first data person in a company
• Providing accurate results
• Receiving insights in a timely manner
• Providing useful insights
• Giving ownership to the team
• Starting as the first data person in a company
• Data For Future podcast
• Supporting team members that are stuck
• Finding Tammy online

Links: 

• Tammy's podcast: https://dataforfuture.org/

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We talked about:

• Mihail’s background
• NLP and self-driving vehicles
• Transitioning from academia to the industry
• Machine learning researchers
• Finding open-ended problems
• Machine learning engineers
• Is data science more engineering or research?
• What can engineers and researchers learn from one another?
• Bridging the disconnect between researchers and engineers
• Breaking down silos
• Fluid roles
• Full-stack data scientists
• Advice to machine learning researchers
• Advice to machine learning engineers
• Reading papers
• Choosing between engineering or research if you’re just starting
• Confetti.ai

Links:

• https://twitter.com/mihail_eric
• http://confetti.ai/

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We talked about:

• Marianna’s background
• Being the only data scientist
• What should already be in the company
• How much experience do you need
• Identifying problems
• Prioritization
• What should the company already know?
• First week
• First month
• First quarter
• Managing expectations
• Solving problems without ML
• Project timelines
• Finding the best solution
• Evaluating performance
• Getting stuck
• Communicating with analysts
• Transitioning from engineering to data science
• Growing the team
• Stopping projects
• Questions for the company
• From research to production
• Wrapping up

Links:

• Marianna's LinkedIn: https://www.linkedin.com/in/marianna-diachuk-53ba60116/

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We talked about:

• Adam’s background
• Adam’s laser and data experience
• Metrics and why do we care about them
• Examples of metrics
• KPIs
• KPI examples
• Derived KPIs
• Creating metrics — grocery store example
• Metric efficiency
• North Star metrics
• Threshold metrics
• Health metrics
• Data team metrics
• Experiments: treatment and control groups
• Accelerate metrics and timeboxing

Links:

• Domino's article about measuring value: http://blog.dominodatalab.com/measuring-data-science-business-value
• Adam's article about skills useful for data scientists: https://towardsdatascience.com/how-to-apply-your-hard-earned-data-science-skillset-812585e3cc06
• Adam's article about standing out: https://towardsdatascience.com/how-to-stand-out-as-a-great-data-scientist-in-2021-3b7a732114a9

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We talked about:

• Natalie’s background
• Airbyte
• What is ETL?
• Why ELT instead of ETL?
• Transformations
• How does ELT help analysts be more independent?
• Data marts and Data warehouses
• Ingestion DB
• ETL vs ELT
• Data lakes
• Data swamps
• Data governance
• Ingestion layer vs Data lake
• Do you need both a Data warehouse and a Data lake?
• Airbyte and ELT
• Modern data stack
• Reverse ETL
• Is drag-and-drop killing data engineering jobs?
• Who is responsible for managing unused data?
• CDC – Change Data Capture
• Slowly changing dimension
• Are there cases where ETL is preferable over ELT?
• Why is Airbyte open source?
• The case of Elasticsearch and AWS

Links:

• Natalie's LinkedIn: https://www.linkedin.com/in/nataliekwong/
• https://airbyte.io/blog/why-the-future-of-etl-is-not-elt-but-el

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We talked about:

• Learning algorithms and data structures
• Resources for learning algorithms and data structures
• Most important data structures
• Learning the abstractions
• Learning algorithms if they aren’t needed at work
• Common mistakes when using wrong data structures
• Importance of data structures for data scientists
• Marcello’s book - Advanced Algorithms and Data Structures
• Bloom filters
• Where Bloom filters are useful
• Approximate nearest neighbours
• Searching for most similar vectors
• Knowing frameworks vs knowing internals of data structures
• Serializing Bloom filters
• Algorithmic problems in job interviews
• Important data structures for data scientists and data engineers
• Learning by doing
• Importance of compiled languages for data scientists

Links:

• Marcello's book: Advanced Algorithms and Data Structures http://mng.bz/eP79 (promo code for 35% discount: poddatatalks21)
• MIT, Introduction to Algorithms: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/
• Algorithms specialization by Tim Roughgarden: https://www.coursera.org/specializations/algorithms

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We talked about:

• Marco’s background
• Role of CDO
• Keeping track of many things
• Becoming a CDO
• Strategy vs tactics
• VP of Data vs CDO
• How many VPs of Data could be there?
• Splitting the work between VP and CDO
• Difference between CTO, CPO, and CDO
• Breaking down the goals and working backwards from them
• Assessing if we’re moving in the right direction
• Dealing with many meetings
• Being more effective
• Building the data-driven culture
• Challenges of working remotely
• Does CDO need deep technical skills?
• Importance of MBA
• The key skills for becoming a CDO
• Biggest challenges within OLX so far
• Demonstrating the CDO skills on a job interview
• Overcoming resistance

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We talked about:

• Mikio’s background
• What Mikio helps with
• Moving from a full-time job to freelancing
• Finding clients and importance of a strong network
• Building a network
• Initial meetings with clients
• Understanding what clients need
• Template for the offer (Million dollar consulting)
• Deciding on rate type: hourly, daily, per project
• Taking vacations (and paying twice for them)
• Avoiding overworking
• Specializing: consulting as a product
• Working full-time as a principal vs being a consultant
• Is the overhead worth it?
• Getting a new client when you already have a project
• After freelancing: what’s next?
• Output of Mikio’s work
• Learning new things
• Lessons learned after finding clients
• Registering as a freelancer in Germany
• Personal liability of a freelancer
• Effect of globalization and remote work on consulting
• Advice for people who want to start freelancing
• Woking full-time and freelancing at the same time

Books:

• Million Dollar Consulting by Alan Weiss
• Built to Sell by John Warrillow

Links:

• Mikio's Twitter: https://twitter.com/mikiobraun
• Mikio's LinkedIn: https://www.linkedin.com/in/mikiobraun/

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Our events: https://datatalks.club/events.html

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We talked about:

• Carmine’s background
• Carmine’s startup FreshFlow
• Doing user research
• Design thinking
• Entrepreneur first
• Finding co-founders: the “expertise edges” framework
• The structure of the EF program
• Coming up with the idea
• How important is going through a startup accelerator?
• Finding your first client
• Finding investors
• Consequences of having a bad investor
• Splitting responsibilities between co-founders
• Hiring
• The importance of delegating
• Making work attractive to hires
• Plans for the future
• Just-in-time supply chain
• What would you have done differently?
• Advice for people starting a startup
• Don’t focus on skills only
• Getting motivation
• Am I ready for a startup?
• Importance of a business school
• Advice on finding a co-founder
• Do I need EF if I already have an idea?
• Having a prototype before the pitch

Books:

• The Mom Test by Rob Fitzpatrick
• Design Thinking by Robert Curedale

Links:

• FreshFlow: https://freshflow.ai/
• Carmine's LinkedIn: https://www.linkedin.com/in/carminepaolino
• Carmine's Twitter: https://twitter.com/paolino

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We don't have an episode lined up for this week, but we recorded a small chat with Vladimir some time ago. Enjoy it! 

We talked about:

• Vladimir's background
• Learning by answering questions
• Don't be afraid of being wrong
• Winnings books
• Learning random things
• Approach learning as a machine learning project

Links:

• Vladimir on LinkedIn: https://www.linkedin.com/in/vladimir-finkelshtein/

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

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We talked about:

• Lina’s background
• What we need to remember when starting a project (checklists)
• Make sure the problem is formalized and close to the core business
• Get the buy-in with stakeholders
• Building trust with stakeholders
• Don’t just focus on upsides – ask about concerns
• Turning a concert into a metric
• What happens when something goes wrong?
• Post mortem reporting
• Apply the 5 why’s
• If a lot of users say it’s a bug – it’s worth investigating
• Post mortem format
• Action points
• Debugging vs explaining the model
• Are there online versions of checklists?
• Make sure to log your inputs
• Talking to end-users and using your own service
• Your ideas vs Stakeholder ideas
• Should data practitioners educate the team about data?
• People skills and ‘dirty’ hacks
• Where to find Lina

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Our events: https://datatalks.club/events.html

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We talked about:

• Ben’s Background
• Building solutions for customers
• Why projects don’t make it to production
• Why do people choose overcomplicated solutions?
• The dangers of isolating data science from the business unit
• The importance of being able to explain things
• Maximizing chances of making into production
• The IKEA effect
• Risks of implementing novel algorithms
• If it can be done simply – do that first
• Don’t become the guinea pig for someone’s white paper
• The importance of stat skills and coding skills
• Structuring an agile team for ML work
• Timeboxing research
• Mentoring
• Ben’s book
• ‘Uncool techniques’ at AI-First companies
• Should managers learn data science?
• Do data scientists need to specialize to be successful?

Links:

• Ben's book: https://www.manning.com/books/machine-learning-engineering-in-action (get 35% off with code "ctwsummer21")

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Our events: https://datatalks.club/events.html

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We talked about:

• Elena’s background
• Why do a startup instead of being an employee?
• Where to get ideas for your startup
• Finding a co-founder
• What should you consider before starting a startup?
• Vertical startup vs infrastructure startup
• ‘AI First’ startups
• Building tools for engineers
• What skills do you need to start a startup?
• Startup risks
• How to be prepared to fail
• Work-life balance
• The part-time startup approach
• Startup investment models
• No resources and no technical expertise – what to do?
• Productionizing your services
• When to hire an expert
• Talking to people with a problem before solving the problem
• Starting Elena’s startup, Evidently
• Elena’s role at Evidently
• Why is Evidently open source?
• “People will just copy my open source code. Should I be concerned?”
• Bottom-up adoption
• Creating value so that clients engage with your product
• Is there a difference between countries when creating a startup?
• Does open source mean the data is safer?
• When should you hire engineers?
• Following the market
• Startups out of genuine interest vs Just for money and for fun

Links:

• EvidentlyAI: https://evidentlyai.com/
• Elena's LinkedIn: https://www.linkedin.com/in/elenasamuylova/
• Elena's Twitter: https://twitter.com/elenasamuylova/

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Our events: https://datatalks.club/events.html

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Links:

• Twitter: https://twitter.com/dead_flowers22
• LinkedIn: https://www.linkedin.com/in/roksolanadiachuk/

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Our events: https://datatalks.club/events.html

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We talked about:

• Andreas’s background
• Why data engineering is becoming more popular
• Who to hire first – a data engineer or a data scientist?
• How can I, as a data scientist, learn to build pipelines?
• Don’t use too many tools
• What is a data pipeline and why do we need it?
• What is ingestion?
• Can just one person build a data pipeline?
• Approaches to building data pipelines for data scientists
• Processing frameworks
• Common setup for data pipelines — car price prediction
• Productionizing the model with the help of a data pipeline
• Scheduling
• Orchestration
• Start simple
• Learning DevOps to implement data pipelines
• How to choose the right tool
• Are Hadoop, Docker, Cloud necessary for a first job/internship?
• Is Hadoop still relevant or necessary?
• Data engineering academy
• How to pick up Cloud skills
• Avoid huge datasets when learning
• Convincing your employer to do data science
• How to find Andreas

Links:

• LinkedIn: https://www.linkedin.com/in/andreas-kretz
• Data engieering cookbook: https://cookbook.learndataengineering.com/
• Course: https://learndataengineering.com/

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We talked about:

• Santiago’s background
• “Transitioning to ML” vs “Adding ML as a skill”
• Getting over the fear of math for software developers
• Learning by explaining
• Seven lessons I learned about starting a career in machine learning
• Lesson 1 – Take the first step
• Lesson 2 – Learning is a marathon, not a sprint
• Lesson 3 – If you want to go quickly, go alone. If you want to go far, go together.
• Lesson 4 – Do something with the knowledge you gain
• Lesson 5 – ML is not just math. Math is not scary.
• Lesson 6 – Your ability to analyze a problem is the most important skill. Coding is secondary.
• Lesson 7 – You don’t need to know every detail
• Tools and frameworks needed to transition to machine learning
• Problem-based learning vs Top-down learning
• Learning resources
• Santiago’s favorite books
• Santiago’s course on transitioning to machine learning
• Improving coding skills
• Building solutions without machine learning
• Becoming a better engineer
• What is the difference between machine learning and data science?
• Getting into machine learning - Reiteration
• Getting past the math

Links:

• Santiago's Twitter: https://twitter.com/svpino
• Santiago's course: https://gumroad.com/svpino#kBjbC
• Pinned tweet with a roadmap: https://twitter.com/svpino/status/1400798154732212230

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Our events: https://datatalks.club/events.html

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Links:

• https://www.notion.so/Analytics-Engineer-New-Role-in-a-Data-Team-9decbf33825c4580967cf3173eb77177
• https://www.linkedin.com/in/victoriaperezmola/

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

Conference: https://datatalks.club/conferences/2021-summer-marathon.html

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We talked about:

• Jessi’s background
• Uri’s background
• Data governance
• Implementing data governance: policies and processes
• Reasons not to have data governance
• Start with “why”
• Cataloging and classifying our data
• Let data work for you
• The human component
• Data quality
• Defining policies
• Implementing policies
• Shopping-card experience for requesting data
• Proving the value of data catalog
• Using data catalog
• Data governance = data catalog?

Links:

• Book: https://www.oreilly.com/library/view/data-governance-the/9781492063483/
• Jessi’s LinkedIn: https://www.linkedin.com/in/jashdown/
• Uri’s LinkedIn: https://linkedin.com/in/ugilad
• Uri’s Twitter: https://twitter.com/ugilad

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

Conference: https://datatalks.club/conferences/2021-summer-marathon.html

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We talked about:

• Yury’s background
• Failing fast: Grammarly for science
• Not failing fast: Keyword recommender
• Four steps to epiphany
• Lesson learned when bringing XGBoost into production
• When data scientists try to be engineers
• Joining a fintech startup: Doing NLP with thousands of GPUs
• Working at a Telco company
• Having too much freedom
• The importance of digital presence
• Work-life balance
• Quantifying impact of failing projects on our CVs
• Business trips to Perm: don’t work on the weekend
• What doesn’t kill you makes you stronger

Links:

• Yury's course: https://mlcourse.ai/
• Yury's Twitter: https://twitter.com/ykashnitsky

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Our events: https://datatalks.club/events.html

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We talked about:

• Data-led academy
• Arpit’s background
• Growth marketing
• Being data-led
• Data-led vs data-driven
• Documenting your data: creating a tracking plan
• Understanding your data
• Tools for creating a tracking plan
• Data flow stages
• Tracking events — examples
• Collecting the data
• Storing and analyzing the data
• Data activation
• Tools for data collection
• Data warehouses
• Reverse ETL tools
• Customer data platforms
• Modern data stack for growth
• Buy vs build
• People we need to in the data flow
• Data democratization
• Motivating people to document data
• Product-led vs data-led

Links:

• https://dataled.academy/

Join our Slack: https://datatalks.club/slack.html

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We talked about:

• Shawn’s background and his book
• Marketing ourselves
• Components of personal marketing
• Personal brand for an average developer
• Picking a domain: what to write about?
• Being too niche
• Finding a good niche
• Learning in public
• Borrowed platforms vs own platform
• Starting on social media: Picking what they put down
• Career transitioning: mutual exchange of value
• Personal marketing for getting a new job
• Getting hired through the back door
• Finding content ideas
• Marketing yourself in public — summary
• Open-source knowledge
• Internal marketing: promoting ourselves at work
• Signature initiative
• Public speaking
• Wrapping up
• Discount for the coding career book
• 75% of the engineering ladder criteria are not technical

Links:

• Shawn's personal page: https://www.swyx.io/
• Twitter: https://twitter.com/swyx
• Book of the week page: https://datatalks.club/books/20210510-the-coding-career-handbook.html (with a discount for DTC members!)

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We talked about:

• Tatiana’s background
• 12 career hacks and changing career
• Hack #1: Change your social circle
• Hack #2: Forget your fears and stereotypes
• Hack #3: Forget distractions
• Hack #4: Don’t overestimate others and don’t underestimate yourself
• Hack #5: Attention genius
• Hack #6: Make a team
• Hack #7: Less is more. Forget about perfectionism
• Hack #8: Initial creation
• Hack #9: Find mentors
• Hack #10: Say “no”
• Hack #11: Look for failures
• Hack #12: Take care of yourself
• Kaggle vs internships and pet projects
• Resources for learning machine learning
• Starting with Kaggle
• Improving focus
• Astroinformatics
• How background in Physics is helpful for transitioning
• Leaving academia
• Preparing for interviews

Links:

• Mock interviews: https://www.pramp.com/
• Learning ML: https://www.coursera.org/learn/machine-learning and https://www.coursera.org/specializations/deep-learning
• Python: https://www.coursera.org/learn/machine-learning-with-python
• SQL: https://www.sqlhabit.com/
• Practice: https://www.kaggle.com/
• MIT 6.006: https://courses.csail.mit.edu/6.006/fall11/notes.shtml
• Coding: https://leetcode.com/
• System design: https://www.educative.io/courses/grokking-the-system-design-interview
• Ukrainian telegram groups for interview preparation: https://t.me/FaangInterviewChannel, https://t.me/FaangTechInterview, https://t.me/FloodInterview

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We talked about:

• Oleg’s background
• Standing out in recruitment process
• NextRound — a service for free mock interviews
• Why rejections are generic
• Starting NextRount — preparing a list of situations
• Steps in the interview process
• Read the job description!
• CV is your landing page
• Take-home assignments
• Questions about your past experience
• Hypothetical case questions
• Technical rounds
• Handling rejections
• What to do after receiving an offer?
• Do recruiters pay attention to age?
• Getting a job with a PhD — it’s a cold start problem
• Should I answer rejection emails?
• Negotiating when my salary is low
• Should I apply for jobs that require 5 years of experience?
• Tricking applicant tracking systems
• What else Oleg learned after interviewing 300 data scientists
• How a horse's ass determined the design of a space shuttle

Links:

• Oleg's service for interviews: https://nextround.cc/
• LinkedIn: https://www.linkedin.com/in/olegnovikov/

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We talked about:

• DataTalks.Club intro
• Lior’s background
• Who is a data strategist?
• Improving communication between business and tech
• Building trust
• Putting data and business people together
• Dealing with pushbacks
• Building things in the lean way (and growing tomatoes)
• Starting with ugly code
• Convincing others to take our code
• MVP vs development and Hummus
• Talking to people who can’t code
• Break down the silos
• Hummus
• Hummus places in Berlin
• Lior’s book: Data is Like a Plate of Hummus
• Data chaos

Links:

• Book: https://www.amazon.com/-/en/Sarah-Mayor/dp/B086L277LZ (can be found on any amazon store)
• Company: https://www.taleaboutdata.com/
• Podcast: https://podcast.whatthedatapodcast.com/
• Linkedin: https://www.linkedin.com/in/liorbarak/
• Twitter: https://twitter.com/liorb

Hummus places in Berlin:

• Azzam: https://goo.gl/maps/uCkb3ATc5CVKapDa6
• Akkawy: https://g.page/akkawy
• The Eatery Berlin: https://g.page/theeateryberlin

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We covered:

• Barr’s background
• Market gaps in data reliability
• Observability in engineering
• Data downtime
• Data quality problems and the five pillars of data observability
• Example: job failing because of a schema change
• Three pillars of observability (good pipelines and bad data)
• Observability vs monitoring
• Finding the root cause
• Who is accountable for data quality? (the RACI framework)
• Service level agreements
• Inferring the SLAs from the historical data
• Implementing data observability
• Data downtime maturity curve
• Monte carlo: data observability solution
• Open source tools
• Test-driven development for data
• Is data observability cloud agnostic?
• Centralizing data observability
• Detecting downstream and upstream data usage
• Getting bad data vs getting unusual data

Links:

• Learn more about Monte Carlo: https://www.montecarlodata.com/
• The Data Engineer's Guide to Root Cause Analysis: https://www.montecarlodata.com/the-data-engineers-guide-to-root-cause-analysis/
• Why You Need to Set SLAs for Your Data Pipelines: https://www.montecarlodata.com/how-to-make-your-data-pipelines-more-reliable-with-slas/
• Data Observability: The Next Frontier of Data Engineering: https://www.montecarlodata.com/data-observability-the-next-frontier-of-data-engineering/
• To get in touch with Barr, ping her in the DataTalks.Club group or use barr@montecarlodata.com

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We talked about:

Andrada’s background

• Recommended courses
• Kaggle and StackOverflow
• Doing notebooks on Kaggle
• Projects for learning data science
• Finding a job and a mentor with Kaggle’s help
• The process for looking for a job
• Main difficulties of getting a job
• Project portfolio and Kaggle
• Helpful analytical skills for transitioning into data science
• Becoming better at coding
• Learning by imitating
• Is doing masters helpful?
• Getting into data science without a masters
• Kaggle is not just about competitions
• The last tip: use social media

Links:

• https://www.kaggle.com/andradaolteanu
• https://twitter.com/andradaolteanuu
• https://www.linkedin.com/in/andrada-olteanu-3806a2132/

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We talked about:

• Knesia’s background
• Data analytics vs data science
• Skills needed for data analytics and data science
• Benefits of getting a masters degree
• Useful online courses
• How project management background can be helpful for the career transition
• Which skills do PMs need to become data analysts?
• Going from working with spreadsheets to working with python
• Kaggle
• Productionizing machine learning models
• Getting experience while studying
• Looking for a job
• Gap between theory and practice
• Learning plan for transitioning
• Last tips and getting involved in projects

Links:

• Notes prepared by Ksenia with all the info: https://www.notion.so/ksenialeg/DataTalks-Club-7597e55f476040a5921db58d48cf718f

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We talked about:

• Demetrious’ background and starting the MLOps community
• Growing MLOps community
• Community moderations and dealing with problems
• Becoming a community and connecting with people
• Feeling belonged
• Managing a community as an introvert
• Keeping communities active
• Doing custdev and talking to users
• Random coffee and meeting with community members
• Organizing community activities
• Is community a business?
• Five steps for starting a community in 2021
• Shameless plug from Demetrious

Links:

• https://mlops.community/

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We talked about:

• Lars’ career
• Doing DataOps before it existed
• What is DataOps
• Data platform
• Main components of the data platform and tools to implement it
• Books about functional programming principles
• Batch vs Streaming
• Maturity levels
• Building self-service tools
• MLOps vs DataOps
• Data Mesh
• Keeping track of transformations
• Lake house

Links:

• https://www.scling.com/reading-list/
• https://www.scling.com/presentations/

Join DataTalks.Club: https://datatalks.club/slack.html​​​

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We talked about:

• Ben’s background
• AI evangelism
• Ben’s first experiences speaking in public
• Becoming a great speaker
• Key Takeaways and Call to Action
• Making a good introduction
• Being Remembered
• Writing a talk proposal for conferences
• Landing a keynote
• Good topics to start talks on
• Pitching a solution talk to meetup organizers
• Top public speaking skill to acquire
• Book recommendations

Join DataTalks.Club: https://datatalks.club/slack.html​​​

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We discussed monetization roles and the capabilities people need to move into those roles.

The key roles are ML Researcher, ML Architect, and ML Product Manager.

We talked about:

Vin's career journey

• What does it mean to "monetize machine learning"
• Important monetization metrics
• Who should we have on the team to make a project successful
• Machine Learning Researcher (applied and scientist) - background, responsibilities, and needed skills
• Developing new categories
• The best recipe for a startup: angry users + data scientists
• What research actually is
• ML Product Manager - background, responsibilities, and needed skills
• How product managers can actually manage all their responsibilities (and they have a lot of them!)
• ML Architect - background, responsibilities, and needed skills
• Path to becoming an architect
• How should we change education to make it more effective
• Important product metrics

And more! 

Links:

• https://twitter.com/v_vashishta​
• https://linkedin.com/in/vineetvashishta​
• https://databyvsquared.com/​

Join DataTalks.Club: https://datatalks.club/slack.html​

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We talked about: 

• Admond's career journey
• What is personal brand
• How Admond started being active online
• Publishing on medium and LinkedIn
• Idea generation process and tools
• Other platforms
• Podcasts
• Offline presence
• 1x1 meetings
• Speaking on conferences
• Having confidence to publish
• Selling online courses
• Personal values
• Admond's course

And many other things

Links:

• https://twitter.com/admond1994
• https://linkedin.com/in/admond1994
• https://buzzsumo.com
• https://feedly.com/
• https://lunchclub.com/
• https://thelead.io/data-scientist-personal-brand-toolkit?utm_medium=instructor&utm_source=admond

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Did you know that there are 3 types different types of data scientists? A for analyst, B for builder, and C for consultant - we discuss the key differences between each one and some learning strategies you can use to become A, B, or C.

We talked about:

• Inspirations for memes
• Danny's background and career journey
• The ABCs of data science - the story behind the idea
• Data scientist type A - Analyst
• Skills, responsibilities, and background for type A
• Transitioning from data analytics to type A data scientist (that's the path Danny took)
• How can we become more curious?
• Data scientist B - Builder
• Responsibilities and background for type B
• Transitioning from type A to type B
• Most important skills for type B
• Why you have to learn more about cloud
• Data scientist type C - consultant
• Skills, responsibilities, and background for type C
• Growing into the C type
• Ideal data science team
• Important business metrics
• Getting a job - easier as type A or type B?
• Looking for a job without experience
• Two approaches for job search: "apply everywhere" and "apply nowhere"
• Are bootcamps useful?
• Learning path to becoming a data scientist
• Danny's data apprenticeship program and "Serious SQL" course
• Why SQL is the most important skill
• R vs Python
• Importance of Masters and PhD

Links:

• Danny's profile on LinkedIn: https://linkedin.com/in/datawithdanny
• Danny's course: https://datawithdanny.com/
• Trailer: https://www.linkedin.com/posts/datawithdanny_datascientist-data-activity-6767988552811847680-GzUK/
• Technical debt paper: https://proceedings.neurips.cc/paper/2015/hash/86df7dcfd896fcaf2674f757a2463eba-Abstract.html

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We talked about:

• How we make decisions with machine learning
• What is decision optimization
• Specifying the decision function
• Emulation for making the best decisions
• Decision optimization and reinforcement learning
• Getting started with decision optimization
• Trends in the industry

Links:

• https://datatalks.club/people/danbecker.html
• https://www.decision.ai/​

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We covered:

• What is a feature store
• Problems it solves
• When to use a feature store
• When not to use a feature store
• The main components
• When a team should start using a feature store

Links:

• Feast: https://feast.dev/
• https://www.tecton.ai/blog/what-is-a-feature-store/
• https://docs.greatexpectations.io/en/latest/reference/core_concepts.html

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We covered:

• What is MLOps
• The difference between MLOps and ML Engineering
• Getting into MLOps
• Kubeflow and its components, ML Platforms
• Learning Kubeflow
• DataOps

And other things

Links:

• Microsoft MLOps maturity model: https://docs.microsoft.com/en-us/azure/architecture/example-scenario/mlops/mlops-maturity-model
• Google MLOps maturity levels: https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
• MLOps roadmap 2020-2025: https://github.com/cdfoundation/sig-mlops/blob/master/roadmap/2020/MLOpsRoadmap2020.md
• Kubeflow website: https://www.kubeflow.org/
• TFX Paper: https://research.google/pubs/pub46484/

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We talked about 

• open source
• getting started with open source
• convincing your employer to contribute to open source
• public speaking
• the checklist for open source projects
• the role of research advocate

And many more things!

Links from Vincent:

• https://www.youtube.com/watch?v=68ABAU_V8qI&t=975s&ab_channel=PyData
• https://www.youtube.com/watch?v=kYMfE9u-lMo&t=958s&ab_channel=PyData
• https://koaning.io/projects.html
• https://calmcode.io/
• https://makenames.io/
• https://koaning.github.io/clumper/api/clumper.html

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We talked about development advocacy for data science.

We covered

• The role of a developer advocate
• The skills needed for the job and the responsibilities
• How to become a developer advocate

You can find Elle on:

• Twitter: https://twitter.com/DrElleOBrien
• LinkedIn: https://linkedin.com/in/drelleobrien
• DVC's youtube channel: https://www.youtube.com/channel/UC37rp97Go-xIX3aNFVHhXfQ

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We talk about blogging technical writing. We cover:

• Why should we write online?
• What should we write about?
• Writing at work: Design documents, wikis, etc.
• The writing process (also at work)

Eugene's website:  eugeneyan.com 

Follow Eugene on Twitter: https://twitter.com/eugeneyan

Suggest topics: https://eugeneyan.com/topic-poll/

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We talked about:

• The role of mentoring in career
• Looking for mentors and preparing for mentoring sessions as a mentee
• Becoming a mentor

And many other things!

Links:

• Rahul's profile on the mentoring club: https://www.mentoring-club.com/the-mentors/rahul-jain
• Rahul's article about mentoring: https://rahulj51.github.io/career/coaching/mentoring/2020/06/22/career-coaching.html

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We covered:

• Getting the recruiter's attention
• Making CV look great
• Tailoring your application to the position

And many other things!

Luke's LinkedIn profile: https://www.linkedin.com/in/lukewhipps/

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We talked about:

• Dat's career so far and the startup he co-founded (Priceloop)
• Who to hire first in a data team
• How to hire the first data scientist

And many other things!

You can find Dat on LinkedIn: https://www.linkedin.com/in/dat-tran-a1602320/

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In this podcast, we talk about CRISP-DM - a methodology for organizing data science projects DataTalks.Club is the place to talk about data. Join our community: https://datatalks.club Read more about CRISP-DM here: https://mlbookcamp.com/article/crisp-dm

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We talked about:

• different roles in a data team: product managers, data analysts, data engineers, data scientists, ML engineers, MLOps engineers

• their responsibilities

• the skills they need

DataTalks.Club is the place to talk about data. Join our community: https://datatalks.club