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Explore the challenges and strategies for managing machine learning models in production environments in this insightful conference talk from ODSC WEST 2015. Delve into the machine learning life-cycle beyond isolated model training, addressing issues such as changing data, competing models, and versioning. Discover unique challenges and opportunities in low-latency, high-availability model serving. Learn about mechanisms for real-time model competition and feedback incorporation. Gain insights on managing technical debt in machine learning through a code-centric approach. Examine these concepts through two systems: one under development at UC Berkeley and another in production at Dato. Benefit from the expertise of Joseph Gonzalez, a postdoc in the AMPLab at UC Berkeley and developer of GraphLab PowerGraph, as he shares his research on distributed systems for machine learning.
