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Building Malleable ML Systems through Measurement, Monitoring & Maintenance

Posted Jul 14
# Explainability and Observability
# Organization and Processes
# Research
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SPEAKER
Karan Goel
Karan Goel
Karan Goel
PhD Student @ Stanford University

Karan Goel is a 3rd year CS PhD student at Stanford advised by Chris RŽ. His main goal is to accelerate the pace at which machine learning can be robustly and safely used in practice across applications, and in industry at large. He leads the Robustness Gym project, where he builds tools to measure, monitor and repair machine learning systems interactively. He is a recipient of the Siebel Foundation Scholarship.

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Karan Goel is a 3rd year CS PhD student at Stanford advised by Chris RŽ. His main goal is to accelerate the pace at which machine learning can be robustly and safely used in practice across applications, and in industry at large. He leads the Robustness Gym project, where he builds tools to measure, monitor and repair machine learning systems interactively. He is a recipient of the Siebel Foundation Scholarship.

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SUMMARY

Machine learning systems are now easier to build than ever, but they still donÕt perform as well as we would hope on real applications. IÕll explore a simple idea in this talk: if ML systems were more malleable and could be maintained like software, we might build better systems. IÕll discuss an immediate bottleneck towards building more malleable ML systems: the evaluation pipeline. IÕll describe the need for finer-grained performance measurement and monitoring, the opportunities paying attention to this area could open up in maintaining ML systems, and some of the tools that IÕm building (with great collaborators) in the Robustness Gym and Meerkat projects to close this gap.

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