https://www.patreon.com/datameshradio (Data Mesh Radio Patreon) - get access to interviews well before they are released Episode list and links to all available episode transcripts (most interviews from #32 on) https://docs.google.com/spreadsheets/d/1ZmCIinVgIm0xjIVFpL9jMtCiOlBQ7LbvLmtmb0FKcQc/edit?usp=sharing (here) Provided as a free resource by DataStax https://www.datastax.com/products/datastax-astra?utm_source=DataMeshRadio (AstraDB); George Trujillo's contact info: email (george.trujillo@datastax.com) and https://www.linkedin.com/in/georgetrujillo/ (LinkedIn) Transcript for this episode (https://docs.google.com/document/d/16yvSgx6S1tMsdEuUlIwNjclOYkw3RugdIldjaK9GnKw/edit?usp=sharing (link)) provided by Starburst. See their Data Mesh Summit recordings https://www.starburst.io/learn/events-webinars/datanova-on-demand/?utm_campaign=starburst-brand&utm_medium=outbound&utm_source=&utm_type=&utm_content=dmradiodnvid&utm_term= (here) and their great data mesh resource center https://www.starburst.io/info/distributed-data-mesh-resource-center/?utm_campaign=starburst-brand&utm_medium=outbound&utm_source=&utm_type=&utm_content=dmradiodmcenter&utm_term= (here). You can download their Data Mesh for Dummies (info gated) https://starburst.io/info/data-mesh-for-dummies/?utm_campaign=starburst-brand&utm_medium=outbound&utm_source=&utm_type=&utm_content=dmradiodnvid&utm_term= (here). In this episode, Scott interviewed Elena Samuylova, Co-Founder and CEO at the ML model monitoring company - and open source project - Evidently AI. This write-up is quite a bit different from other recent episode write-ups. Scott has added a lot of color on not just what was said but how it could apply to data and analytics work, especially for data mesh. Some key takeaways/thoughts this time specifically from Scott's point of view: A good rule of software that applies to ML and data, especially mesh data products: "If you build it, it will break." Set yourself up to react to that. Maintenance may not be "sexy" but it's probably the most crucial aspect of ML and data in general. It's very easy to create a data asset and move on. But doing the work to maintain is really treating things like a product. ML models are inherently expected to degrade. When they degrade - for a number of reasons - they must be retrained or replaced. Similarly, on the mesh data product side, we need to think about monitoring for degradation to figure out if they are still valuable or how to increase value. Data drift - changes in the information input into your model, e.g. a new prospect base - can cause a model to not perform well, especially against this new segment of prospects. That data drift detection could actually be a very useful insight to pass on as an insight - has something changed with our demographics? If so, what? When? Do we know why? Concept drift - the real world has changed so your model is not performing as expected - is a crucial concept in data and analytics too. Are we still sharing information about the things that matter? In a way that is understandable? Are we encapsulating what's happening in the real world in our mesh data products? Concept drift feels similar to semantic drift in the analytics world. So we can look to potentially take deeper learnings from how people approach and combat concept drift from ML and apply it to data mesh. How can we monitor degradation in mesh data products and prevent that degradation our data and analytics work? Historically, reports drifted further and further from reality with no intervention because the pain of change was so high. Are we fully reliant on the domain to know? Can we use software to help us detect semantic drift? Very early days on that one. ML models are designed to do one thing very well. Unfortunately, we don't have a good framework for reuse at the model level in ML. Maybe at the ML feature level? ML models have expected...