Feature Stores for MLOps with Mike Del Balso

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About this Episode

Today we're joined by Mike del Balso, co-Founder and CEO of Tecton. Mike, who you might remember from our last conversation on the podcast, was a foundational member of the Uber team that created their ML platform, Michelangelo. Since his departure from the company in 2018, he has been busy building up Tecton, and their enterprise feature store. In our conversation, Mike walks us through why he chose to focus on the feature store aspects of the machine learning platform, the journey, personal and otherwise, to operationalizing machine learning, and the capabilities that more mature platforms teams tend to look for or need to build. We also explore the differences between standalone components and feature stores, if organizations are taking their existing databases and building feature stores with them, and what a dynamic, always available feature store looks like in deployment. Finally, we explore what sets Tecton apart from other vendors in this space, including enterprise cloud providers who are throwing their hat in the ring.
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Thanks to our sponsor Tecton

Built by the creators of Uber's Michelangelo ML Platform, Tecton's mission is to bring machine learning intelligence to every production application. Tecton offers a key piece of the ML stack, a feature platform, designed to operate and manage the data flows for operational ML applications. It allows data scientists and engineers to manage feature definitions as code, orchestrate pipelines to continually calculate fresh feature values, build high-quality training data sets, and serve features in production for real-time inference. Tecton enables teams to deploy ML applications in days instead of months, and currently serves customers ranging from leading technology and Fortune 50 companies to a wide range of start-ups. And Tecton's open source offering, Feast, is the leading open source feature store. To learn more about Tecton, visit Tecton.ai.

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