ML Observability: Critical Piece of the ML Stack

August 11 2021, 10:15 am - 10:25 am (10 Minutes)

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.


Aparna Dhinakaran

Chief Product Officer

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 bachelors 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 PhD program at Cornell University.