Presentation: Instrumentation, Observability & Monitoring of Machine Learning Models

Track: Predictive Architectures in the Real World

Location: Cyril Magnin I + II

Duration: 10:40am - 11:20am

Day of week: Tuesday

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Abstract

Production machine learning involves intentionally deploying and running some of the ugliest, hardest-to-debug spaghetti code that you have ever seen (i.e., code that was generated by a computer) into the critical path of your operational environment. Because so much of machine learning code has an academic origin and most experienced practitioners have primarily worked in offline, batch-oriented computing environments, there is often an impedance mismatch between devops and machine learning practitioners that causes unnecessary pain for everyone involved. In this talk, we're going to go deep into the monitoring and visibility needs of machine learning models in order to bridge these gaps and make everyone's working life a bit simpler, more pleasant, and more productive.

Speaker: Josh Wills

Software Engineer, Search, Learning, and Intelligence @SlackHQ

Software Engineer working on Search and Learning @SlackHQ.

Find Josh Wills at

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