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ML Observability: Critical Piece of the ML Stack

Posted Jul 14
# Explainability and Observability
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SPEAKER
Aparna Dhinakaran
Aparna Dhinakaran
Aparna Dhinakaran
Co-Founder & Chief Product Officer @ Arize AI

Aparna Dhinakaran is the Co-Founder and Chief Product Officer at Arize AI, a pioneer, and early leader in machine learning (ML) observability. A frequent speaker at top conferences and thought leader in the space, Dhinakaran was recently named to the Forbes 30 Under 30. Before Arize, Dhinakaran was an ML engineer and leader at Uber, Apple, and TubeMogul (acquired by Adobe). During her time at Uber, she built several core ML Infrastructure platforms, including Michealangelo. She has a bachelorÕs 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.

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Aparna Dhinakaran is the Co-Founder and Chief Product Officer at Arize AI, a pioneer, and early leader in machine learning (ML) observability. A frequent speaker at top conferences and thought leader in the space, Dhinakaran was recently named to the Forbes 30 Under 30. Before Arize, Dhinakaran was an ML engineer and leader at Uber, Apple, and TubeMogul (acquired by Adobe). During her time at Uber, she built several core ML Infrastructure platforms, including Michealangelo. She has a bachelorÕs 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.

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SUMMARY

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. In this talk, Aparna Dhinakaran, Co-Founder, CPO of Arize AI (Ex-Uber Machine Learning), will discuss the state of the commonly seen ML Production monitoring and its challenges. She will focus on how to use statistical distance checks to monitor features and model output in production, how to analyze the changes effects on models and how to use explainability techniques to determine if issues are model or data related.

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