The Rise of ML Observability

What Every ML Practitioner Needs To Know About Improving Model Performance


Detecting, diagnosing and resolving ML model performance can be difficult for even the most sophisticated ML engineers. Join Arize co-founders Aparna Dhinakaran and Jason Lopatecki as they reflect on the evolution of ML observability since pioneering the space over one year ago and demo the Arize AI platform publicly for the first time. 

In this session, we will explore:

  • The challenges of productionalizing ML 
  • Why an evaluation store is becoming a critical piece of the ML infrastructure stack
  • The four pillars of ML observability and how to tackle each: drift, performance analysis, data quality and explainability

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Aparna Dhinakaran
Co-founder & Chief Product Officer

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.

Jason Lopatecki
Arize Founder

A respected executive and software engineer with a history of building and scaling data-centric businesses and products, Jason was most recently was co-founder and chief innovation officer at TubeMogul where he saw the company through its successful IPO and acquisition by Adobe.

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