Building Malleable ML Systems through Measurement, Monitoring & Maintenance

apply(meetup) - Aug '21 - 10 minutes

Machine learning systems are now easier to build than ever, but they still don’t perform as well as we would hope on real applications. I’ll explore a simple idea in this talk: if ML systems were more malleable and could be maintained like software, we might build better systems. I’ll discuss an immediate bottleneck towards building more malleable ML systems: the evaluation pipeline. I’ll 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 I’m building (with great collaborators) in the Robustness Gym and Meerkat projects to close this gap.

Karan Goel

PhD Student

Stanford University

Karan Goel is a 3rd year CS PhD student at Stanford advised by Chris Ré. 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.