

Feature Stores for MLOps with Mike del Balso - #420
20 snips Oct 19, 2020
In this engaging conversation, Mike del Balso, Co-founder and CEO of Tecton, shares insights on feature stores and their critical role in MLOps. He discusses his journey from creating Uber's ML platform, Michelangelo, to building Tecton. Mike delves into the essential components of an effective machine learning stack and highlights the differences between standalone components and feature stores. The discussion also touches on deployment strategies, the importance of data consistency, and what makes Tecton's offering unique in a competitive landscape.
AI Snips
Chapters
Transcript
Episode notes
Mike's Background
- At Google, Mike Del Balso worked on productionized machine learning systems for ad auctions.
- At Uber, he helped build Michelangelo from the ground up, starting with few production models.
Unexpected Value of Data Workflows
- Building data workflows into Uber's ML platform unexpectedly unlocked significant value, enabling faster production and feature reuse.
- This led to rapid scaling of machine learning across Uber as teams shared and reused features, leading to a "Cambrian explosion."
Feature Store Benefits and Concerns
- Leverage pre-built features in a feature store to foster feature reusability and avoid redundant work by data scientists.
- Address concerns about feature ownership and maintenance by clarifying responsibilities and SLAs within the feature store's metadata.