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In the latest installment of our Data-Centric AI series, we’re joined by friend of the show Mike Del Balso, Co-founder and CEO of Tecton. If you’ve heard any of our other conversations with Mike, you know we spend a lot of time discussing feature stores, or as he now refers to them, feature platforms. We explore the current complexity of data infrastructure broadly and how that has changed over the last five years, as well as the maturation of streaming data platforms. We discuss the wide vs deep paradox that exists around ML tooling, and the idea around the “ML Flywheel”, a strategy that leverages data to accelerate machine learning. Finally, we spend time discussing internal ML team construction, some of the challenges that organizations face when building their ML platforms teams, and how they can avoid the pitfalls as they arise.
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.
