The O'Reilly Data Show Podcast: Recent Episodes
O'Reilly Radar
The O'Reilly Data Show explores the opportunities and techniques driving big data and data science. Through interviews and analysis, we highlight the people putting data to work.
In this episode of the Data Show, I speak with Peter Bailis, founder and CEO of Sisu, a startup that is using machine learning to improve operational analytics. Bailis is also an assistant professor of computer science at Stanford University, where he conducts research into data-intensive systems and where he is co-founder of the DAWN Lab.
In this episode of the Data Show, I speak with Arun Kejariwal of Facebook and Ira Cohen of Anodot (full disclosure: I’m an advisor to Anodot). This conversation stemmed from a recent online panel discussion we did, where we discussed time series data, and, specifically, anomaly detection and forecasting. Both Kejariwal (at Machine Zone, Twitter, and Facebook) and Cohen (at HP and Anodot) have extensive experience building analytic and machine learning solutions at large scale, and both have worked extensively with time-series data. The growing interest in AI and machine learning has not been confined to computer vision, speech technologies, or text. In the enterprise, there is strong interest in using similar automation tools for temporal data and time series.
In this episode of the Data Show, I speak with Michael Mahoney, a member of RISELab, the International Computer Science Institute, and the Department of Statistics at UC Berkeley. A physicist by training, Mahoney has been at the forefront of many important problems in large-scale data analysis. On the theoretical side, his works spans algorithmic and statistical methods for matrices, graphs, regression, optimization, and related problems. On the applications side, he has contributed to systems used for internet and social media analysis, social network analysis, as well as for a host of applications in the physical and life sciences. Most recently, he has been working on deep neural networks, specifically developing theoretical methods and practical diagnostic tools that should be helpful to practitioners who use deep learning.
In this episode of the Data Show, I speak with Kesha Williams, technical instructor at A Cloud Guru, a training company focused on cloud computing. As a full stack web developer, Williams became intrigued by machine learning and started teaching herself the ML tools on Amazon Web Services. Fast forward to today, Williams has built some well-regarded Alexa skills, mastered ML services on AWS, and has now firmly added machine learning to her developer toolkit.
In this episode of the Data Show, I speak with Alex Ratner, project lead for Stanford’s Snorkel open source project; Ratner also recently garnered a faculty position at the University of Washington and is currently working on a company supporting and extending the Snorkel project. Snorkel is a framework for building and managing training data. Based on our survey from earlier this year, labeled data remains a key bottleneck for organizations building machine learning applications and services.
Ratner was a guest on the podcast a little over two years ago when Snorkel was a relatively new project. Since then, Snorkel has added more features, expanded into computer vision use cases, and now boasts many users, including Google, Intel, IBM, and other organizations. Along with his thesis advisor professor Chris Ré of Stanford, Ratner and his collaborators have long championed the importance of building tools aimed squarely at helping teams build and manage training data. With today’s release of Snorkel version 0.9, we are a step closer to having a framework that enables the programmatic creation of training data sets.
In this episode of the Data Show, I speak with Cassie Kozyrkov, technical director and chief decision scientist at Google Cloud. She describes "decision intelligence" as an interdisciplinary field concerned with all aspects of decision-making, and which combines data science with the behavioral sciences. Most recently she has been focused on developing best practices that can help practitioners make safe, effective use of AI and data. Kozyrkov uses her platform to help data scientists develop skills that will enable them to connect data and AI with their organizations' core businesses.
We had a great conversation spanning many topics, including:
How data science can be more useful
The importance of the human side of data
The leadership talent shortage in data science
Is data science a bubble?
In this episode of the Data Show, I spoke with Roger Chen, co-founder and CEO of Computable Labs, a startup focused on building tools for the creation of data networks and data exchanges. Chen has also served as co-chair of O'Reilly's Artificial Intelligence Conference since its inception in 2016. This conversation took place the day after Chen and his collaborators released an interesting new white paper, "Fair value and decentralized governance of data." Current-generation AI and machine learning technologies rely on large amounts of data, and to the extent they can use their large user bases to create “data silos,” large companies in large countries (like the U.S. and China) enjoy a competitive advantage. With that said, we are awash in articles about the dangers posed by these data silos. Privacy and security, disinformation, bias, and a lack of transparency and control are just some of the issues that have plagued the perceived owners of “data monopolies.”
In this week's episode of the Data Show, we're featuring an interview Data Show host Ben Lorica participated in for the Software Engineering Daily Podcast, where he was interviewed by Jeff Meyerson. Their conversation mainly centered around data engineering, data architecture and infrastructure, and machine learning (ML).
In this episode of the Data Show, I spoke with Nick Pentreath, principal engineer at IBM. Pentreath was an early and avid user of Apache Spark, and he subsequently became a Spark committer and PMC member. Most recently his focus has been on machine learning, particularly deep learning, and he is part of a group within IBM focused on building open source tools that enable end-to-end machine learning pipelines.
In this episode of the Data Show, I spoke with Dhruba Borthakur (co-founder and CTO) and Shruti Bhat (SVP of Marketing) of Rockset, a startup focused on building solutions for interactive data science and live applications. Borthakur was the founding engineer of HDFS and creator of RocksDB, while Bhat is an experienced product and marketing executive focused on enterprise software and data products. Their new startup is focused on a few trends I’ve recently been thinking about, including the re-emergence of real-time analytics, and the hunger for simpler data architectures and tools. Borthakur exemplifies the need for companies to continually evaluate new technologies: while he was the founding engineer for HDFS, these days he mostly works with object stores like S3.
In this episode of the Data Show, I spoke with Jike Chong, chief data scientist at Acorns, a startup focused on building tools for micro-investing. Chong has extensive experience using analytics and machine learning in financial services, and he has experience building data science teams in the U.S. and in China.
We had a great conversation spanning many topics, including:
Potential applications of data science in financial services.
The current state of data science in financial services in both the U.S. and China.
His experience recruiting, training, and managing data science teams in both the U.S. and China.
In this episode of the Data Show, I spoke with Jeff Jonas, CEO, founder and chief scientist of Senzing, a startup focused on making real-time entity resolution technologies broadly accessible. He was previously a fellow and chief scientist of context computing at IBM. Entity resolution (ER) refers to techniques and tools for identifying and linking manifestations of the same entity/object/individual. Ironically, ER itself has many different names (e.g., record linkage, duplicate detection, object consolidation/reconciliation, etc.).
ER is an essential first step in many domains, including marketing (cleaning up databases), law enforcement (background checks and counterterrorism), and financial services and investing. Knowing exactly who your customers are is an important task for security, fraud detection, marketing, and personalization. The proliferation of data sources and services has made ER very challenging in the internet age. In addition, many applications now increasingly require near real-time entity resolution.
We had a great conversation spanning many topics including:
Why ER is interesting and challenging
How ER technologies have evolved over the years
How Senzing is working to democratize ER by making real-time AI technologies accessible to developers
Some early use cases for Senzing’s technologies
Some items on their research agenda
In this episode of the Data Show, I spoke with Neelesh Salian, software engineer at Stitch Fix, a company that combines machine learning and human expertise to personalize shopping. As companies integrate machine learning into their products and systems, there are important foundational technologies that come into play. This shouldn’t come as a shock, as current machine learning and AI technologies require large amounts of data—specifically, labeled data for training models. There are also many other considerations—including security, privacy, reliability/safety—that are encouraging companies to invest in a suite of data technologies. In conversations with data engineers, data scientists, and AI researchers, the need for solutions that can help track data lineage and provenance keeps popping up.
There are several San Francisco Bay Area companies that have embarked on building data lineage systems—including Salian and his colleagues at Stitch Fix. I wanted to find out how they arrived at the decision to build such a system and what capabilities they are building into it.
In this episode of the Data Show, I spoke with Avner Braverman, co-founder and CEO of Binaris, a startup that aims to bring serverless to web-scale and enterprise applications. This conversation took place shortly after the release of a seminal paper from UC Berkeley (“Cloud Programming Simplified: A Berkeley View on Serverless Computing”), and this paper seeded a lot of our conversation during this episode.
In this episode of the Data Show, I spoke with Forough Poursabzi-Sangdeh, a postdoctoral researcher at Microsoft Research New York City. Poursabzi works in the interdisciplinary area of interpretable and interactive machine learning. As models and algorithms become more widespread, many important considerations are becoming active research areas: fairness and bias, safety and reliability, security and privacy, and Poursabzi’s area of focus—explainability and interpretability.
In this episode of the Data Show, I spoke with Kartik Hosanagar, professor of technology and digital business, and professor of marketing at The Wharton School of the University of Pennsylvania. Hosanagar is also the author of a newly released book, "A Human’s Guide to Machine Intelligence," an interesting tour through the recent evolution of AI applications, which draws from his extensive experience at the intersection of business and technology.
In this episode of the Data Show, I spoke with P.W. Singer, strategist and senior fellow at the New America Foundation, and a contributing editor at Popular Science. He is co-author of an excellent new book, LikeWar: The Weaponization of Social Media, which explores how social media has changed war, politics, and business. The book is essential reading for anyone interested in how social media has become an important new battlefield in a diverse set of domains and settings.
In this episode of the Data Show, I spoke with Siwei Lyu, associate professor of computer science at the University at Albany, State University of New York. Lyu is a leading expert in digital media forensics, a field of research into tools and techniques for analyzing the authenticity of media files. Over the past year, there have been many stories written about the rise of tools for creating fake media (mainly images, video, audio files). Researchers in digital image forensics haven’t exactly been standing still, though. As Lyu notes, advances in machine learning and deep learning have also found a receptive audience among the forensics community.
In this episode of the Data Show, I spoke with Maryam Jahanshahi, research scientist at TapRecruit, a startup that uses machine learning and analytics to help companies recruit more effectively. In an upcoming survey, we found that a “skills gap” or “lack of skilled people” was one of the main bottlenecks holding back adoption of AI technologies. Many companies are exploring a variety of internal and external programs to train staff on new tools and processes. The other route is to hire new talent. But recent reports suggest that demand for data professionals is strong and competition for experienced talent is fierce. Jahanshahi and her team are building natural language and statistical tools that can help companies improve their ability to attract and retain talent across many key areas.
In this episode of the Data Show, I spoke with Andrew Burt, chief privacy officer and legal engineer at Immuta, a company building data management tools tuned for data science. Burt and cybersecurity pioneer Daniel Geer recently released a must-read white paper (“Flat Light”) that provides a great framework for how to think about information security in the age of big data and AI. They list important changes to the information landscape and offer suggestions on how to alleviate some of the new risks introduced by the rise of machine learning and AI.
We discussed their new white paper, cybersecurity (Burt was previously a special advisor at the FBI), and an exciting new Strata Data tutorial that Burt will be co-teaching in March.
In this episode of the Data Show, I spoke with Haoyuan Li, CEO and founder of Alluxio, a startup commercializing the open source project with the same name (full disclosure: I’m an advisor to Alluxio). Our discussion focuses on the state of Alluxio (the open source project that has roots in UC Berkeley’s AMPLab), specifically emerging use cases here and in China. Given the large-scale use in China, I also wanted to get Li’s take on the state of data and AI technologies in Beijing and other parts of China.
For the end-of-year holiday episode of the Data Show, I turned the tables on Data Show host Ben Lorica to talk about trends in big data, machine learning, and AI, and what to look for in 2019. Lorica also showcased some highlights from our upcoming Strata Data and Artificial Intelligence conferences.
In this episode of the Data Show, I spoke with Alex Wong, associate professor at the University of Waterloo, and co-founder of DarwinAI, a startup that uses AI to address foundational challenges with deep learning in the enterprise. As the use of machine learning and analytics become more widespread, we’re beginning to see tools that enable data scientists and data engineers to scale and tackle many more problems and maintain more systems. This includes automation tools for the many stages involved in data science, including data preparation, feature engineering, model selection, and hyperparameter tuning, as well as tools for data engineering and data operations.
Wong and his collaborators are building solutions for enterprises, including tools for generating efficient neural networks and for the performance analysis of networks deployed to edge devices.
In this episode of the Data Show, I spoke with Vitaly Gordon, VP of data science and engineering at Salesforce. As the use of machine learning becomes more widespread, we need tools that will allow data scientists to scale so they can tackle many more problems and help many more people. We need automation tools for the many stages involved in data science, including data preparation, feature engineering, model selection and hyperparameter tuning, as well as monitoring.
I wanted the perspective of someone who is already faced with having to support many models in production. The proliferation of models is still a theoretical consideration for many data science teams, but Gordon and his colleagues at Salesforce already support hundreds of thousands of customers who need custom models built on custom data. They recently took their learnings public and open sourced TransmogrifAI, a library for automated machine learning for structured data, which sits on top of Apache Spark.
In this episode of the Data Show, I spoke with Francesca Lazzeri, an AI and machine learning scientist at Microsoft, and her colleague Jaya Mathew, a senior data scientist at Microsoft. We conducted a couple of surveys this year—“How Companies Are Putting AI to Work Through Deep Learning” and “The State of Machine Learning Adoption in the Enterprise”—and we found that while many companies are still in the early stages of machine learning adoption, there’s considerable interest in moving forward with projects in the near future. Lazzeri and Mathew spend a considerable amount of time interacting with companies that are beginning to use machine learning and have experiences that span many different industries and applications. I wanted to learn some of the processes and tools they use when they assist companies in beginning their machine learning journeys.
In this episode of the Data Show, I spoke with Alon Kaufman, CEO and co-founder of Duality Technologies, a startup building tools that will allow companies to apply analytics and machine learning to encrypted data. In a recent talk, I described the importance of data, various methods for estimating the value of data, and emerging tools for incentivizing data sharing across organizations. As I noted, the main motivation for improving data liquidity is the growing importance of machine learning. We’re all familiar with the importance of data security and privacy, but probably not as many people are aware of the emerging set of tools at the intersection of machine learning and security. Kaufman and his stellar roster of co-founders are doing some of the most interesting work in this area.
In this episode of the Data Show, I spoke with Jacob Ward, a Berggruen Fellow at Stanford University. Ward has an extensive background in journalism, mainly covering topics in science and technology, at National Geographic, Al Jazeera, Discovery Channel, BBC, Popular Science, and many other outlets. Most recently, he’s become interested in the interplay between research in psychology, decision-making, and AI systems. He’s in the process of writing a book on these topics, and was gracious enough to give an informal preview by way of this podcast conversation.
In this episode of the Data Show, I spoke with Sharad Goel, assistant professor at Stanford, and his student Sam Corbett-Davies. They recently wrote a survey paper, “A Critical Review of Fair Machine Learning,” where they carefully examined the standard statistical tools used to check for fairness in machine learning models. It turns out that each of the standard approaches (anti-classification, classification parity, and calibration) has limitations, and their paper is a must-read tour through recent research in designing fair algorithms. We talked about their key findings, and, most importantly, I pressed them to list a few best practices that analysts and industrial data scientists might want to consider.
In this episode of the Data Show, I spoke with Alan Nichol, co-founder and CTO of Rasa, a startup that builds open source tools to help developers and product teams build conversational applications. About 18 months ago, there was tremendous excitement and hype surrounding chatbots, and while things have quieted lately, companies and developers continue to refine and define tools for building conversational applications. We spoke about the current state of chatbots, specifically about the types of applications developers are building today and how he sees conversational applications evolving in the near future.
In this episode of the Data Show, I spoke with Eric Jonas, a postdoc in the new Berkeley Center for Computational Imaging. Jonas is also affiliated with UC Berkeley’s RISE Lab. It was at a RISE Lab event that he first announced Pywren, a framework that lets data enthusiasts proficient with Python run existing code at massive scale on Amazon Web Services. Jonas and his collaborators are working on a related project, NumPyWren, a system for linear algebra built on a serverless architecture. Their hope is that by lowering the barrier to large-scale (scientific) computation, we will see many more experiments and research projects from communities that have been unable to easily marshal massive compute resources. We talked about Bayesian machine learning, scientific computation, reinforcement learning, and his stint as an entrepreneur in the enterprise software space.
In this episode of the Data Show, I spoke with Harish Doddi, co-founder and CEO of Datatron, a startup focused on helping companies deploy and manage machine learning models. As companies move from machine learning prototypes to products and services, tools and best practices for productionizing and managing models are just starting to emerge. Today’s data science and data engineering teams work with a variety of machine learning libraries, data ingestion, and data storage technologies. Risk and compliance considerations mean that the ability to reproduce machine learning workflows is essential to meet audits in certain application domains. And as data science and data engineering teams continue to expand, tools need to enable and facilitate collaboration.
As someone who specializes in helping teams turn machine learning prototypes into production-ready services, I wanted to hear what Doddi has learned while working with organizations that aspire to “become machine learning companies.”
In this episode of the Data Show, I spoke with Chang Liu, applied research scientist at Georgian Partners. In a previous post, I highlighted early tools for privacy-preserving analytics, both for improving decision-making (business intelligence and analytics) and for enabling automation (machine learning). One of the tools I mentioned is an open source project for SQL-based analysis that adheres to state-of-the-art differential privacy (a formal guarantee that provides robust privacy assurances). Since business intelligence typically relies on SQL databases, this open source project is something many companies can already benefit from today.
What about machine learning? While I didn’t have space to point this out in my previous post, differential privacy has been an area of interest to many machine learning researchers. Most practicing data scientists aren’t aware of the research results, and popular data science tools haven’t incorporated differential privacy in meaningful ways (if at all). But things will change over the next months. For example, Liu wants to make ideas from differential privacy accessible to industrial data scientists, and she is part of a team building tools to make this happen.
In this episode of the Data Show, I spoke with Andrew Feldman, founder and CEO of Cerebras Systems, a startup in the blossoming area of specialized hardware for machine learning. Since the release of AlexNet in 2012, we have seen an explosion in activity in machine learning, particularly in deep learning. A lot of the work to date happened primarily on general purpose hardware (CPU, GPU). But now that we’re six years into the resurgence in interest in machine learning and AI, these new workloads have attracted technologists and entrepreneurs who are building specialized hardware for both model training and inference, in the data center or on edge devices.
In fact, companies with enough volume have already begun building specialized processors for machine learning. But you have to either use specific cloud computing platforms or work at specific companies to have access to such hardware. A new wave of startups (including Cerebras) will make specialized hardware affordable and broadly available. Over the next 12-24 months architects and engineers will need to revisit their infrastructure and decide between general purpose or specialized hardware, and cloud or on-premise gear.
ARTIFICIAL INTELLIGENCE CONFERENCE
The Artificial Intelligence Conference in San Francisco, September 4-7, 2018 Early price ends July 20. In light of the training duration and cost they face using current (general purpose) hardware, some experiments might be hard to justify. Upcoming specialized hardware will enable data scientists to try out ideas that they previously would have hesitated to pursue. This will surely lead to more research papers and interesting products as data scientists are able to run many more experiments (on even bigger models) and iterate faster.
As founder of one of the most anticipated hardware startups in the deep learning space, I wanted get Feldman’s views on the challenges and opportunities faced by engineers and entrepreneurs building hardware for machine learning workloads.
In this episode of the Data Show, I spoke with Aurélie Pols of Mind Your Privacy, one of my go-to resources when it comes to data privacy and data ethics. This interview took place at Strata Data London, a couple of days before the EU General Data Protection Regulation (GDPR) took effect. I wanted her perspective on this landmark regulation, as well as her take on trends in data privacy and growing interest in ethics among data professionals.
In this episode of the Data Show, I spoke with Andrew Burt, chief privacy officer at Immuta, and Steven Touw, co-founder and CTO of Immuta. Burt recently co-authored an upcoming white paper on managing risk in machine learning models, and I wanted to sit down with them to discuss some of the proposals they put forward to organizations that are deploying machine learning.
Some high-profile examples of models gone awry have raised awareness among companies for the need for better risk management tools and processes. There is now a growing interest in ethics among data scientists, specifically in tools for monitoring bias in machine learning models. In a previous post, I listed some of the key considerations organization should keep in mind as they move models to production, but the upcoming report co-authored by Burt goes far beyond and recommends lines of defense, including a description of key roles that are needed.
In this episode of the Data Show, I spoke with Ashok Srivastava, senior vice president and chief data officer at Intuit. He has a strong science and engineering background, combined with years of applying machine learning and data science in industry. Prior to joining Intuit, he led the teams responsible for data and artificial intelligence products at Verizon. I wanted his perspective on a range of issues, including the role of the chief data officer, ethics in machine learning, and the emergence of AI technologies for enterprise products and applications.
This episode of the Data Show marks our 100th episode. This podcast stemmed out of video interviews conducted at O’Reilly’s 2014 Foo Camp. We had a collection of friends who were key members of the data science and big data communities on hand and we decided to record short conversations with them. We originally conceived of using those initial conversations to be the basis of a regular series of video interviews. The logistics of studio interviews proved too complicated, but those Foo Camp conversations got us thinking about starting a podcast, and the Data Show was born.
To mark this milestone, my colleague Paco Nathan, co-chair of Jupytercon, turned the tables on me and asked me questions about previous Data Show episodes. In particular, we examined the evolution of key topics covered in this podcast: data science and machine learning, data engineering and architecture, AI, and the impact of each of these areas on businesses and companies. I’m proud of how this show has reached so many people across the world, and I’m looking forward to sharing more conversations in the future.
In this episode of the Data Show, I spoke with Jason Dai, CTO of Big Data Technologies at Intel, and one of my co-chairs for the AI Conference in Beijing. I wanted to check in on the status of BigDL, specifically how companies have been using this deep learning library on top of Apache Spark, and discuss some newly added features. It turns out there are quite a number of companies already using BigDL in production, and we talked about some of the popular uses cases he’s encountered. We recorded this podcast while we were at the AI Conference in Beijing, so I wanted to get Dai’s thoughts on the adoption of AI technologies among Chinese companies and local/state government agencies.
In this episode of the Data Show, I spoke with Jerry Overton, senior principal and distinguished technologist at DXC Technology. I wanted the perspective of someone who works across industries and with a variety of companies. I specifically wanted to explore the current state of data science and AI within companies and public sector agencies. As much as we talk about use cases, technologies, and algorithms, there are also important issues that practitioners like Overton need to address, including privacy, security, and ethics. Overton has long been involved in teaching and mentoring new data scientists, so we also discussed some tips and best practices he shares with new members of his team.
In this episode of the Data Show, I spoke with Guillaume Chaslot, an ex-YouTube engineer and founder of AlgoTransparency, an organization dedicated to helping the public understand the profound impact algorithms have on our lives. We live in an age when many of our interactions with companies and services are governed by algorithms. At a time when their impact continues to grow, there are many settings where these algorithms are far from transparent. There is growing awareness about the vast amounts of data companies are collecting on their users and customers, and people are starting to demand control over their data. A similar conversation is starting to happen about algorithms—users are wanting more control over what these models optimize for and an understanding of how they work.
I first came across Chaslot through a series of articles about the power and impact of YouTube on politics and society. Many of the articles I read relied on data and analysis supplied by Chaslot. We talked about his work trying to decipher how YouTube’s recommendation system works, filter bubbles, transparency in machine learning, and data privacy.
In this episode of the Data Show, I spoke Jesse Anderson, managing director of the Big Data Institute, and my colleague Paco Nathan, who recently became co-chair of Jupytercon. This conversation grew out of a recent email thread the three of us had on machine learning engineers, a new job role that LinkedIn recently pegged as the fastest growing job in the U.S. In our email discussion, there was some disagreement on whether such a specialized job role/title was needed in the first place. As Eric Colson pointed out in his beautiful keynote at Strata Data San Jose, when done too soon, creating specialized roles can slow down your data team.
We recorded this conversation at Strata San Jose, while Anderson was in the middle of teaching his very popular two-day training course on real-time systems. We closed the conversation with Anderson’s take on Apache Pulsar, a very impressive new messaging system that is starting to gain fans among data engineers.
In this episode of the Data Show, I spoke with Ameet Talwalkar, assistant professor of machine learning at CMU and co-founder of Determined AI. He was an early and key contributor to Spark MLlib and a member of AMPLab. Most recently, he helped conceive and organize the first edition of SysML, a new academic conference at the intersection of systems and machine learning (ML).
We discussed using and deploying deep learning at scale. This is an empirical era for machine learning, and, as I noted in an earlier article, as successful as deep learning has been, our level of understanding of why it works so well is still lacking. In practice, machine learning engineers need to explore and experiment using different architectures and hyperparameters before they settle on a model that works for their specific use case. Training a single model usually involves big (labeled) data and big models; as such, exploring the space of possible model architectures and parameters can take days, weeks, or even months. Talwalkar has spent the last few years grappling with this problem as an academic researcher and as an entrepreneur. In this episode, he describes some of his related work on hyperparameter tuning, systems, and more.
In this episode of the Data Show, I spoke with Ofer Ronen, GM of Chatbase, a startup housed within Google’s Area 120. With tools for building chatbots becoming accessible, conversational interfaces are becoming more prevalent. As Ronen highlights in our conversation, chatbots are already enabling companies to automate many routine tasks (mainly in customer interaction). We are still in the early days of chatbots, but if current trends persist, we’ll see bots deployed more widely and take on more complex tasks and interactions. Gartner recently predicted that by 2021, companies will spend more on bots and chatbots than mobile app development.
Like any other software application, as bots get deployed in real-world applications, companies will need tools to monitor their performance. For a single, simple chatbot, one can imagine developers manually monitoring log files for errors and problems. Things get harder as you scale to more bots and as the bots get increasingly more complex. As in the case of other machine learning applications, when companies start deploying many more chatbots, automated tools for monitoring and diagnostics become essential.
The good news is relevant tools are beginning to emerge. In this episode, Ronen describes a tool he helped build: Chatbase is a chatbot analytics and optimization service that leverages machine learning research and technologies developed at Google. In essence, Chatbase lets companies focus on building and deploying the best possible chatbots.
In this episode of the Data Show, I spoke with Danny Lange, VP of AI and machine learning at Unity Technologies. Lange previously led data and machine learning teams at Microsoft, Amazon, and Uber, where his teams were responsible for building data science tools used by other developers and analysts within those companies. When I first heard that he was moving to Unity, I was curious as to why he decided to join a company whose core product targets game developers.
As you’ll glean from our conversation, Unity is at the forefront of some of the most exciting, practical applications of deep learning (DL) and reinforcement learning (RL). Realistic scenery and imagery are critical for modern games. GANs and related semi-supervised techniques can ease content creation by enabling artists to produce realistic images much more quickly. In a previous post, Lange described how reinforcement learning opens up the possibility of training/learning rather than programming in game development.
Lange explains why simulation environments are going to be important tools for AI developers. We are still in the early days of machine intelligence, and I am looking forward to more tools that can democratize AI research (including future releases by Lange and his team at Unity).
In this episode of the Data Show, I spoke with Leo Meyerovich, co-founder and CEO of Graphistry. Graphs have always been part of the big data revolution (think of the large graphs generated by the early social media startups). In recent months, I’ve come across companies releasing and using new tools for creating, storing, and (most importantly) analyzing large graphs. There are many problems and use cases that lend themselves naturally to graphs, and recent advances in hardware and software building blocks have made large-scale analytics possible.
Starting with his work as a graduate student at UC Berkeley, Meyerovich has pioneered the combination of hardware and software acceleration to create truly interactive environments for visualizing large amounts of data. Graphistry has built a suite of tools that enables analysts to wade through large data sets and investigate business and security incidents. The company is currently focused on the security domain—where it turns out that graph representations of data are things security analysts are quite familiar with.
In this episode of the Data Show, I spoke with Mark Hammond, founder and CEO of Bonsai, a startup at the forefront of developing AI systems in industrial settings. While many articles have been written about developments in computer vision, speech recognition, and autonomous vehicles, I’m particularly excited about near-term applications of AI to manufacturing, robotics, and industrial automation. In a recent post, I outlined practical applications of reinforcement learning (RL)—a type of machine learning now being used in AI systems. In particular, I described how companies like Bonsai are applying RL to manufacturing and industrial automation. As researchers explore new approaches for solving RL problems, I expect many of the first applications to be in industrial automation.
In this episode of the Data Show, I spoke with Fabian Yamaguchi, chief scientist at ShiftLeft. His 2015 Ph.D. dissertation sketched out how the combination of static analysis, graph mining, and machine learning, can be used to develop tools to augment security analysts. In a recent post, I argued for machine learning tools to augment teams responsible for deploying and managing models in production (machine learning engineers). These are part of a general trend of using machine learning to develop and manage the software systems of tomorrow. Yamaguchi’s work is step one in this direction: using machine learning to reduce the number of security vulnerabilities in complex software products.
In this episode of the Data Show, I spoke with Kristian Hammond, chief scientist of Narrative Science and professor of EECS at Northwestern University. He has been at the forefront of helping companies understand the power, limitations, and disruptive potential of AI technologies and tools. In a previous post on machine learning, I listed types of uses cases (a taxonomy) for machine learning that could just as well apply to enterprise applications of AI. But how do you identify good use cases to begin with?
In this episode of the Data Show, I spoke with Tim Kraska, associate professor of computer science at MIT. To take advantage of big data, we need scalable, fast, and efficient data management systems. Database administrators and users often find themselves tasked with building index structures (“indexes” in database parlance), which are needed to speed up data access.
In this episode of the Data Show, I spoke with Christine Hung, head of data solutions at Spotify. Prior to joining Spotify, she led data teams at the NY Times and at Apple (iTunes). Having led teams at three different companies, I wanted to hear her thoughts on digital transformation, and I wanted to know how she approaches the challenge of building, managing, and nurturing data teams.
I also wanted to learn more about what goes into building a recommender system for a popular consumer service like Spotify. Engagement should clearly be the most important metric, but there are other considerations, such as introducing users to new or “long tail” content.
In this episode of the Data Show, I spoke with Neha Narkhede, co-founder and CTO of Confluent. As I noted in a recent post on “The Age of Machine Learning,” data integration and data enrichment are non-trivial and ongoing challenges for most companies. Getting data ready for analytics—including machine learning—remains an area of focus for most companies. It turns out, “data lakes” have become staging grounds for data; more refinement usually needs to be done before data is ready for analytics. By making it easier to create and productionize data refinement pipelines on both batch and streaming data sources, analysts and data scientists can focus on analytics that can unlock value from data.
When I first discovered and started using Apache Spark, a majority of the use cases I used it for involved unstructured text. The absence of libraries meant rolling my own NLP utilities, and, in many cases, implementing a machine learning library (this was pre deep learning, and MLlib was much smaller). I’d always wondered why no one bothered to create an NLP library for Spark when many people were using Spark to process large amounts of text. The recent, early success of BigDL confirms that users like the option of having native libraries.
In this episode of the Data Show, I spoke with David Talby of Pacific.AI, a consulting company that specializes in data science, analytics, and big data. A couple of years ago I mentioned the need for an NLP library within Spark to Talby; he not only agreed, he rounded up collaborators to build such a library. They eventually carved out time to build the newly released Spark NLP library. Judging by the reception received by BigDL and the number of Spark users faced with large-scale text processing tasks, I suspect Spark NLP will be a standard tool among Spark users.
STRATA DATA CONFERENCE
Strata Data Conference in San Jose, March 5-8, 2018 Registration is now open Talby and I also discussed his work helping companies build, deploy, and monitor machine learning models. Tools and best practices for model development and deployment are just beginning to emerge—I summarized some of them in a recent post, and, in this episode, I discussed these topics with a leading practitioner.
In this episode of the Data Show, I spoke with Rhea Liu, analyst at China Tech Insights, a new research firm that is part of Tencent’s Online Media Group. If there’s one place where AI and machine learning are discussed even more than the San Francisco Bay Area, that would be China. Each time I go to China, there are new applications that weren’t widely available just the year before. This year, it was impossible to miss bike sharing, mobile payments seemed to be accepted everywhere, and people kept pointing out nascent applications of computer vision (facial recognition) to identity management and retail (unmanned stores).
In this episode of the Data Show, I spoke with Bruno Fernandez-Ruiz, co-founder and CTO of Nexar. We first met when he was leading Yahoo! technical teams charged with delivering a variety of large-scale, real-time data products. His new company is helping build out critical infrastructure for the emerging transportation sector.
In this episode of the Data Show, I spoke with Carme Artigas, co-founder and CEO of Synergic Partners (a Telefonica company). As more companies adopt big data technologies and techniques, it’s useful to remember that the end goal is to extract information and insight. In fact, as with any collection of tools and technologies, the main challenge is identifying and prioritizing use cases.
In this episode of the Data Show, we look back to a recent conversation I had at the Spark Summit in San Francisco with Ion Stoica (UC Berkeley professor and executive chairman of Databricks) and Matei Zaharia (assistant professor at Stanford and chief technologist of Databricks). Stoica and Zaharia were core members of UC Berkeley’s AMPLab, which originated Apache Spark, Apache Mesos, and Alluxio.
In this episode of the Data Show, I spoke with Ken Stanley, founding member of Uber AI Labs and associate professor at the University of Central Florida. Stanley is an AI researcher and a leading pioneer in the field of neuroevolution—a method for evolving and learning neural networks through evolutionary algorithms. In a recent survey article, Stanley went through the history of neuroevolution and listed recent developments, including its applications to reinforcement learning problems.
In this episode of the Data Show, I spoke with Robert Nishihara and Philipp Moritz, graduate students at UC Berkeley and members of RISE Lab. I wanted to get an update on Ray, an open source distributed execution framework that makes it easy for machine learning engineers and data scientists to scale reinforcement learning and other related continuous learning algorithms. Many AI applications involve an agent (for example a robot or a self-driving car) interacting with an environment. In such a scenario, an agent will need to continuously learn the right course of action to take for a specific state of the environment.
In this episode of the Data Show, I spoke with Soumith Chintala, AI research engineer at Facebook. Among his many research projects, Chintala was part of the team behind DCGAN (Deep Convolutional Generative Adversarial Networks), a widely cited paper that introduced a set of neural network architectures for unsupervised learning. Our conversation centered around PyTorch, the successor to the popular Torch scientific computing framework. PyTorch is a relatively new deep learning framework that is fast becoming popular among researchers. Like Chainer, PyTorch supports dynamic computation graphs, a feature that makes it attractive to researchers and engineers who work with text and time-series.
In this episode of the Data Show, I spoke with Evangelos Simoudis, co-founder of Synapse Partners and a frequent contributor to O’Reilly. He recently published a book entitled "The Big Data Opportunity in Our Driverless Future," and I wanted get his thoughts on the transportation industry and the role of big data and analytics in its future. Simoudis is an entrepreneur, and he also advises and invests in many technology startups. He became interested in the automotive industry long before the current wave of autonomous vehicle startups was in the planning stages.
In this episode of the Data Show, I spoke with Grace Huang, data science lead at Pinterest. With its combination of a large social graph, enthusiastic users, and multimedia data, I’ve long regarded Pinterest as a fascinating lab for data science. Huang described the challenge of building a sustainable content ecosystem and shared lessons from the front lines of machine learning product launches. We also discussed recommenders, the emergence of deep learning as a technique used within Pinterest, and the role of data science within the company.
In this episode of the Data Show, I speak with Naveen Rao, VP and GM of the Artificial Intelligence Products Group at Intel. In an earlier episode, we learned that scaling current deep learning models requires innovations in both software and hardware. Through his startup Nervana (since acquired by Intel), Rao has been at the forefront of building a next generation platform for deep learning and AI.
I wanted to get his thoughts on what the future infrastructure for machine learning would look like. At least for now, we’re seeing a variety of approaches, and many companies are using heterogeneous processors (even specialized ones) and proprietary interconnects for deep learning. Nvidia and Intel Nervana are set to release processors that excel at both training and inference, but as Rao pointed out, at large-scale there are many considerations—including utilization, power consumption, and convenience—that come into play.
In this episode of the Data Show, I spoke with Michael Freedman, CTO of Timescale and professor of computer science at Princeton University. When I first heard that Freedman and his collaborators were building a time-series database, my immediate reaction was: “Don’t we have enough options already?” The early incarnation of Timescale was a startup focused on IoT, and it was while building tools for the IoT problem space that Freedman and the rest of the Timescale team came to realize that the database they needed wasn’t available (at least out in open source). Specifically, they wanted a database that could easily support complex queries and the sort of real-time applications many have come to associate with streaming platforms. Based on early reactions to TimescaleDB, many users concur.
In this episode of the Data Show, I spoke with Geoffrey Bradway, VP of engineering at Numerai, a new hedge fund that relies on contributions of external data scientists. The company hosts regular competitions where data scientists submit machine learning models for classification tasks. The most promising submissions are then added to an ensemble of models that the company uses to trade in real-world financial markets.
In this episode of the Data Show, I spoke with Alex Ratner, a graduate student at Stanford and a member of Christopher Ré’s Hazy research group. Training data has always been important in building machine learning algorithms, and the rise of data-hungry deep learning models has heightened the need for labeled data sets. In fact, the challenge of creating training data is ongoing for many companies; specific applications change over time, and what were gold standard data sets may no longer apply to changing situations.
In this episode of the Data Show, I spoke with Jeremy Stanley, VP of data science at Instacart, a popular grocery delivery service that is expanding rapidly. As Stanley describes it, Instacart operates a four-sided marketplace comprised of retail stores, products within the stores, shoppers assigned to the stores, and customers who order from Instacart. The objective is to get fresh groceries from popular retailers delivered to customers in a timely fashion. Instacart’s goals land them in the center of the many opportunities and challenges involved in building high-impact data products.
In this episode of the Data Show, I spoke with David Ferrucci, founder of Elemental Cognition and senior technologist at Bridgewater Associates. Ferrucci served as principal investigator of IBM’s DeepQA project and led the Watson team that became champion of the Jeopardy! quiz show. Elemental Cognition (EC) is a research group focused on building an AI system that will be equipped with state-of-the-art natural language understanding technologies. Ferrucci envisions that EC will ship with foundational knowledge in many subject areas, but will be able to very quickly acquire knowledge in other (specialized) domains with the help of “human mentors.”
Having built and deployed several prominent AI systems through the years, I also wanted to get Ferrucci’s perspective on the evolution of AI technologies, and how enterprises can take advantage of all the exciting recent developments.
In this episode of the Data Show, I spoke with Lukas Biewald, co-founder and chief data scientist at CrowdFlower. In a previous episode we covered how the rise of deep learning is fueling the need for large labeled data sets and high-performance computing systems. CrowdFlower has a service that many leading companies have come to rely on to provide them with labeled data sets to train machine learning models. As deep learning models get larger and more complex, they require training data sets that are bigger than those required by other machine learning techniques.
In this episode of the Data Show, I spoke with Reza Zadeh, adjunct professor at Stanford University, co-organizer of ScaledML, and co-founder of Matroid, a startup focused on commercial applications of deep learning and computer vision. Zadeh also is the co-author of the forthcoming book TensorFlow for Deep Learning (now in early release). Our conversation took place on the eve of the recent ScaledML conference, and much of our conversation was focused on practical and real-world strategies for scaling machine learning. In particular, we spoke about the rise of deep learning, hardware/software interfaces for machine learning, and the many commercial applications of computer vision.
In this episode of the Data Show, I spoke with Karthik Ramasamy, adjunct faculty member at UC Berkeley, former engineering manager at Twitter, and co-founder of Streamlio. Ramasamy managed the team that built Heron, an open source, distributed stream processing engine, compatible with Apache Storm. While Ramasamy has seen firsthand what it takes to build and deploy large-scale distributed systems (within Twitter, he worked closely with the team that built DistributedLog), he is first and foremost interested in designing and building end-to-end applications. As someone who organizes many conferences, I’m all too familiar with the vast array of popular big data frameworks available. But, I also know that engineers and architects are most interested in content and material that helps them cut through the options and decisions.
In this episode of the Data Show, I spoke with Aurélien Géron, a serial entrepreneur, data scientist, and author of a popular, new book entitled "Hands-on Machine Learning with Scikit-Learn and TensorFlow." Géron’s book is aimed at software engineers who want to learn machine learning and start deploying machine learning models in real-world products.
In this episode of the Data Show, I spoke with Francisco Webber, founder of Cortical.io, a startup that is applying tools based on Hierarchical Temporal Memory (HTM) to natural language understanding. While HTM has been around for more than a decade, there aren’t many companies that have released products based on it (at least compared to other machine learning methods). Numenta, an organization developing open source machine intelligence based on the biology of the neocortex, maintains a community site featuring showcase applications. Webber’s company has been building tools based on HTM and applying them to big text data in a variety of industries; financial services has been a particularly strong vertical for Cortical.
In this special episode of the Data Show, O'Reilly's Jenn Webb speaks with Max Ogden, director of Code for Science and Society. Recently, Max and Code for Science have been working on the ongoing rescue of data.gov and assisting with other data rescue projects, such as Data Refuge; they’re also the nonprofit developers supporting Dat, a data versioning and distribution manager, which came out of Max’s work making government and scientific data open and accessible.
In this episode of the Data Show, I spoke with Anima Anandkumar, a leading machine learning researcher, and currently a principal research scientist at Amazon. I took the opportunity to get an update on the latest developments on the use of tensors in machine learning. Most of our conversation centered around MXNet—an open source, efficient, scalable deep learning framework. I’ve been a fan of MXNet dating back to when it was a research project out of CMU and UW, and I wanted to hear Anandkumar’s perspective on its recent progress as a framework for enterprises and practicing data scientists.
In this episode of the Data Show, I spoke with Parvez Ahammad, who leads the data science and machine learning efforts at Instart Logic. He has applied machine learning in a variety of domains, most recently to computational neuroscience and security. Along the way, he has assembled and managed teams of data scientists and has had to grapple with issues like explainability and interpretability, ethics, insufficient amount of labeled data, and adversaries who target machine learning models. As more companies deploy machine learning models into products, it’s important to remember there are many other factors that come into play aside from raw performance metrics.
In this episode of the Data Show, I spoke with Jason Dai, CTO of big data technologies at Intel, and co-chair of Strata + Hadoop World Beijing. Dai and his team are prolific and longstanding contributors to the Apache Spark project. Their early contributions to Spark tended to be on the systems side and included Netty-based shuffle, a fair-scheduler, and the “yarn-client” mode. Recently, they have been contributing tools for advanced analytics. In partnership with major cloud providers in China, they’ve written implementations of algorithmic building blocks and machine learning models that let Apache Spark users scale to extremely high-dimensional models and large data sets. They achieve scalability by taking advantage of things like data sparsity and Intel’s MKL software. Along the way, they’ve gained valuable experience and insight into how companies deploy machine learning models in real-world applications.
As data scientists add deep learning to their arsenals, they need tools that integrate with existing platforms and frameworks. This is particularly important for those who work in large enterprises. In this episode of the Data Show, I spoke with Adam Gibson, co-founder and CTO of Skymind, and co-creator of Deeplearning4J (DL4J). Gibson has spent the last few years developing the DL4J library and community, while simultaneously building deep learning solutions and products for large enterprises.
Specialists describe deep learning as akin to a rocketship that needs a really big engine (a model) and a lot of fuel (the data) in order to go anywhere interesting. To get a better understanding of the issues involved in building compute systems for deep learning, I spoke with one of the foremost experts on this subject: Greg Diamos, senior researcher at Baidu. Diamos has long worked to combine advances in software and hardware to make computers run faster. In recent years, he has focused on scaling deep learning to help advance the state-of-the-art in areas like speech recognition.
This episode consists of excerpts from a recent talk I gave at a conference commemorating the end of the UC Berkeley AMPLab project. This section pertained to some recent trends in Data and AI. For a complete list of trends we’re watching in 2017, as well as regular doses of highly curated resources, subscribe to our Data and AI newsletters.
In this episode I spoke with Ion Stoica, cofounder and chairman of Databricks. Stoica is also a professor of computer science at UC Berkeley, where he serves as director of the new RISE Lab (the successor to AMPLab). Fresh off the incredible success of AMPLab, RISE seeks to build tools and platforms that enable sophisticated real-time applications on live data, while maintaining strong security. As Stoica points out, users will increasingly expect security guarantees on systems that rely on online machine learning algorithms that make use of personal or proprietary data.
In this episode I spoke with Vikash Mansinghka, research scientist at MIT, where he leads the Probabilistic Computing Project, and co-founder of Empirical Systems. I’ve long wanted to introduce listeners to recent developments in probabilistic programming, and I found the perfect guide in Mansinghka.
In this episode I spoke with Michael Franklin, co-director of UC Berkeley’s AMPLab and chair of the Department of Computer Science at the University of Chicago. AMPLab is well-known in the data community for having originated Apache Spark, Alluxio (formerly Tachyon) and many other open source tools. Today marks the start of a two-day symposium commemorating the end of AMPLab, and we took the opportunity to reflect on its impressive accomplishments.
In this special two-segment episode of the Data Show, I spoke with Dafna Shahaf, assistant professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem. Her area of research is focused on tools and techniques for overcoming information overload, an area of increasing importance in an attention economy. With the upcoming U.S. Presidential Elections right around the corner, I included a conversation between Jenn Webb, host of the O’Reilly Radar Podcast, and Sam Wang, co-founder of the Princeton Election Consortium and professor of neuroscience and molecular biology at Princeton University.
In this episode of the O’Reilly Data Show, I spoke with Christopher Nguyen, CEO and co-founder of Arimo. Nguyen and Arimo were among the first adopters and proponents of Apache Spark, Alluxio, and other open source technologies. Most recently, Arimo’s suite of analytic products has relied on deep learning to address a range of business problems.
In this episode of the O’Reilly Data Show, O’Reilly’s online managing editor Jenn Webb speaks with Natalino Busa on the topic of predictive analytics, the challenges of feature engineering, and a new class of techniques that is enabling features to emerge from patterns within the data. They also discuss the relationship between predictive techniques and high-quality microservices, and how machine learning is being used to improve financial services.
Ask a random person for an example of an AI system and chances are he or she will name self-driving vehicles. In this episode of the O’Reilly Data Show, I sat down with Shaoshan Liu, co-founder of PerceptIn and previously the senior architect (autonomous driving) at Baidu USA. We talked about the technology behind self-driving vehicles, their reliance on rule-based decision engines, and deploying large-scale deep learning systems.
In this episode of the O’Reilly Data Show I sat down with O’Reilly author Dean Wampler, big data architect at Lightbend. We talked about new architectures for stream processing, Scala, and cloud computing.
In this episode of the O’Reilly Data Show, I spoke withMichael Li, cofounder and CEO of the Data Incubator. We discussed the current state of data science and data engineering training programs, Apache Spark, quantitative finance, and the misunderstanding around the term “data science.”
While I was in Beijing for Strata + Hadoop World, several people reminded me of the chatbot Xiaoice—one of the most popular accounts on the Chinese social media site Weibo. Developed by Microsoft researchers, Xiaoice comes with a personality and is able to engage users in extended conversations on Weibo. These types of capabilities highlight that in an attention economy, systems that are able to forge an emotional connection will garner more loyalty and engagement from users.
In this episode of the O’Reilly Data Show, I sat down with Rana el Kaliouby, co-founder and CEO of Affectiva, one of the leading experts in emotion sensing systems. We talked about the impact of deep learning and computer vision, Affectiva’s large facial expression database, and privacy and ethics in an era of multimodal systems.
In this episode of the O’Reilly Data Show, I spoke with Adam Marcus, co-founder and CTO of B12, a startup focused on building human-in-the-loop intelligent applications. We talked about the open source platform Orchestra,for coordinating human-in-the-loop projects; the current wave of human-assisted AI applications; best practices for reviewing and scoring experts; and flash teams.
In this episode of the O’Reilly Data Show, I spoke withJana Eggers, CEO of Nara Logics. Eggers’ involvement with AI dates back to her days as a researcher at the Los Alamos National Laboratory. Most recently she has been helping companies across many industries adopt AI technologies as a way to enable a range of intelligent data applications.
In this episode of the O’Reilly Data Show, I spoke with John Akred, cofounder and CTO of Silicon Valley Data Science. Akred and his colleagues teach two of the more popular Strata + Hadoop World tutorials—“Developing a Modern Enterprise Data Strategy” and “Architecting a Data Platform.” We talked about his career in data science and consulting, and his penchant for bringing emerging technologies and tools into large enterprises.
In this episode of the O’Reilly Data Show, I spoke with Yishay Carmiel, president of Spoken Labs. As voice becomes a common user interface, the need for accurate and intelligent speech technologies has grown. And although computer vision is a common entry point for deep learning, some of the most interesting commercial applications of deep neural networks are in speech recognition. Carmiel has spent several years building commercial speech applications, and along the way he has witnessed (and helped architect) massive improvements in speech technologies.
In this episode of the O’Reilly Data Show, I spoke with Rajat Monga, who serves as a director of engineering at Google and manages the TensorFlow engineering team. We talked about how he ended up working on deep learning, the current state of TensorFlow, and the applications of deep learning to products at Google and other companies.
In this episode of the O’Reilly Data Show, I spoke with data management industry veteran Rohit Jain, currently the CTO of Esgyn. We talked about his years at HP Labs, and his recent project to bring hybrid transactional/analytic technologies into the Hadoop ecosystem.
In this episode of the Data Show, I spoke with Mike Tung, founder and CEO of Diffbot - a company dedicated to building large-scale knowledge databases. Diffbot is at the heart of many web applications, and it’s starting to power a wide array of intelligent applications. We talked about the challenges of building a web-scale platform for doing highly accurate, semi-supervised, structured data extraction. We also took a tour through the AI landscape, and the early days of self-driving cars.
With the release of Spark version 2.0, streaming starts becoming much more accessible to users. By adopting a continuous processing model (on an infinite table), the developers of Spark have enabled users of its SQL or DataFrame APIs to extend their analytic capabilities to unbounded streams.
Within the Spark community, Databricks Engineer, Michael Armbrust is well-known for having led the long-term project to move Spark’s interactive analytics engine from Shark to Spark SQL. (Full disclosure: I’m an advisor to Databricks.) Most recently he has turned his efforts to helping introduce a much simpler stream processing model to Spark Streaming (“structured streaming”).
In this episode of the O’Reilly Data Show, I spoke with Danny Bickson, co-founder and VP at Dato, and the principal organizer of the Data Science Summit (full disclosure: I’m a member of the conference organizing committee). Among machine learning students and practitioners, recommender systems have become somewhat of a canonical use case and application. One of the early and popular building blocks was GraphLab’s collaborative filtering toolkit, a library originally written and maintained by Bickson. He has continued to keep tabs on the latest developments in recommenders and continues to help organize workshops on related topics throughout the world.
In this episode of the O’Reilly Data Show, I spoke with Ira Cohen, co-founder and chief data scientist at Anodot (full disclosure: I’m an advisor to Anodot). Since my days in quantitative finance, I’ve had a longstanding interest in time-series analysis. Back then, I used statistical (and data mining) techniques on relatively small volumes of financial time series. Today’s applications and use cases involve data volumes and speeds that require a new set of tools for data management, collection, and simple analysis.
On the analytics side, applications are also beginning to require online machine learning algorithms that are able to scale, are adaptive, and free of a rigid dependence on labeled data. I talked with Cohen about the challenges in building an advanced analytics system for intelligent applications at extremely large scale.
In this episode of the O’Reilly Data Show, I spoke with Mikio Braun, delivery lead and data scientist at Zalando. After spending previous years in academia, Braun recently made the decision to switch to industry. He shared some observations about building large-scale systems, particularly deploying data applications in production systems. Given his longstanding background as a machine learning researcher and practitioner, I wanted to get his take on topics like deep learning, hybrid systems, feature engineering, and AI applications.
In this episode of the O’Reilly Data Show, I spoke with one of Strata + Hadoop World’s most popular teachers—Duncan Ross, data and analytics director at TES Global. In his long career in data, Ross has seen several stages of the evolution of tools, techniques, and training programs, and along the way he has interacted with business managers in many countries and regions. In keeping with his wide-ranging interests, we discussed many topics, including business analytics, data science training programs, data philanthropy and data for good, and university rankings.
In this episode of the O’Reilly Data Show, I spoke with M.C. Srivas, co-founder of MapR and currently chief architect for data at Uber. We discussed his long career in data management and his experience building a variety of distributed systems. In the course of his career, Srivas has architected key components that now comprise many data platforms (distributed file system, database, query engine, messaging system, etc.).
In this episode of the O’Reilly Data Show, I spoke with Fang Yu, co-founder and CTO of DataVisor. We discussed her days as a researcher at Microsoft, the application of data science and distributed computing to security, and hiring and training data scientists and engineers for the security domain.
Ben Lorica speaks with one of the most popular speakers at Strata+Hadoop World: Joe Hellerstein, professor of Computer Science at UC Berkeley and co-founder/CSO of Trifacta. They talk about his past and current academic research (which spans HCI, databases, and systems), data wrangling, large-scale distributed systems, and his recent work on metadata services.
O'Reilly's Ben Lorica chats with Eric Colson, chief algorithms officer at Stitch Fix, and former VP of data science and engineering at Netflix. They talk about building and deploying mission-critical, human-in-the-loop systems for consumer Internet companies.
In this episode of the O’Reilly Data Show, Ben Lorica sat down with Vasant Dhar, a professor at the Stern School of Business and Center for Data Science at NYU, founder of SCT Capital Management, and editor-in-chief of the Big Data Journal (full disclosure: Lorica is a member of the editorial board). They talked about the early days of A.I. and data mining, and recent applications of data science to financial investing and other domains.
In this special holiday episode of the O’Reilly Data Show, I look back at two conversations I had earlier this year at the Spark Summit in San Francisco. The first segment is an on-stage fireside chat with Ben Horowitz, co-founder of Andreessen Horowitz and author of The Hard Thing About Hard Things.
In the second segment, Reynold Xin, one of the architects of Apache Spark, explains the rise of Apache Spark in China.
Evan Chan, distinguished engineer at Tuplejump, on the early days of Spark (particularly his contributions to Spark/Cassandra integration), his interesting new open source project (FiloDB), and recent trends in cloud computing.
Emil Eifrem, CEO and co-founder of Neo Technology, on the early days of NoSQL, applications of graph databases, cloud computing, and company culture in the U.S. and Sweden.
Jai Ranganathan, senior director of product management at Cloudera, on trends in the Hadoop ecosystem, cloud computing, the recent surge in interest in all things real time, and hardware trends
Google engineer Tyler Akidau on the evolution of stream processing, the challenges of building systems that scale to massive data sets, and the recent surge in interest in all things real time.
Evangelos Simoudis on data mining, investing in data startups, and corporate innovation.
Todd Lipcon on hybrid and specialized tools in distributed systems, and unveiling Kudu.
O'Reilly's Ben Lorica chats with Mike Cafarella, assistant professor of computer science at the University of Michigan and co-founder of both Hadoop and Nutch.
O'Reilly's Ben Lorica chats with Alice Zheng about her background, techniques for evaluating machine learning models, how much math data scientists need to know, and the art of interacting with business users.
O'Reilly's Ben Lorica chats with David Epstein about his book, data science and sports, and his recent series of articles detailing suspicious practices at one of the world’s premier track and field training programs (the Oregon Project).
O'Reilly's Ben Lorica chats with Poppy Crum, neuroscientist and researcher at Dolby Labs, about AI and virtual reality systems, and about building a team of researchers from diverse disciplines.
O'Reilly's Ben Lorica chats with UC Berkeley assistant professor Ben Recht about optimization, compressed sensing, and large-scale machine learning pipelines.
O'Reilly's Ben Lorica chats with Ihab Ilyas, professor at the University of Waterloo and co-founder of Tamr, about how he started working on data cleaning tools, academic database research, and training computer science students for positions in industry.
O'Reilly's Ben Lorica chats with Phil Liu, co-founder and CTO of SignalFx, about hiring and building teams in the age of cloud computing, building tools for monitoring large numbers of time series, and lessons he’s learned from managing teams at leading technology companies.
O'Reilly's Ben Lorica chats with Patrick Wendell, release manager of Apache Spark and co-founder of Databricks, about how he came to join the UC Berkeley AMPLab, the current state of Spark ecosystem components, Spark’s future roadmap, and interesting applications built on top of Spark.
O'Reilly's Ben Lorica chats with Gary Kazantsev, who runs the R&D Machine Learning group at Bloomberg LP, about how big data and data science are making a difference in finance.