Increase Model Telemetry and Improve AI Outcomes: Applications of Machine Learning Observability


Machine learning models have become increasingly complex, and it is imperative to utilize better tools to monitor, troubleshoot, and explain their decisions as models move from research to production environments. In this workshop, Aparna Dhinakaran, Co-Founder, CPO of Arize AI (Ex-Uber Machine Learning), will discuss the state of ML production monitoring, its challenges, and how to actively improve models in production.

Learn how to validate degradation in model performance, take a deep dive to investigate the root causes of those inaccurate predictions, and set up proactive monitors to mitigate the impact of future degradations. Experience ML observability first hand with a walkthrough of the Arize platform using practical use case examples to identify segments where your model is underperforming, troubleshoot root cause analysis, proactively monitor for future degradations, and estimate the business impact of a models decisions


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 several 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.

Open Data Science




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