ML Observability 101: How To Make Your Models Work IRL

 

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What is ML Observability?

Will your model work in production? Why isn’t your model performing the way you thought it would? What’s wrong, why?

Successfully taking a machine learning model from research to production is hard. As more and more machine learning models are deployed into production, it is imperative we have better observability tools to monitor, troubleshoot, and explain their decisions.

ML Observability helps you eliminate the guesswork and deliver continuous model improvements. Learn how to:

Use statistical distance checks to monitor features and model output in production

Analyze performance regressions such as drift and how it impacts business metrics

Use troubleshooting techniques to determine if issues are model or data related
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Aparna Dhinakaran, CPO at Arize AI  

Aparna Dhinakaran is Chief Product Officer at Arize AI, a startup focused on ML Observability. She was previously an ML engineer at Uber, Apple, and Tubemogul (acquired by Adobe). During her time at Uber, she built a number of core ML Infrastructure platforms including Michaelangelo. She has a bachelor's from Berkeley's Electrical Engineering and Computer Science program where she published research with Berkeley's AI Research group. She is on a leave of absence from the Computer Vision Ph.D. program at Cornell University.

Arize AI 2021