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 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 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

Jason Lopatecki is co-founder and CEO of Arize AI, a machine learning observability company. He is a garage-to-IPO executive with an extensive background in building marketing-leading products and businesses that heavily leverage analytics. Prior to Arize, Jason was co-founder and chief innovation officer at TubeMogul where he scaled the business into a public company and eventual acquisition by Adobe. Jason has hands-on knowledge of big data architectures, programmatic advertising systems, distributed systems, and machine learning and data processing architectures. In his free time, Jason tinkers with personal machine learning projects as a hobby, with a special interest in unsupervised learning and deep neural networks. He holds an electrical engineering and computer science degree from UC Berkeley - Go Bears!

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