Aparna Dhinakaran
Aparna Dhinakaran is the Co-Founder and Chief Product Officer at Arize AI, a pioneer and early leader in AI & Agent observability and evaluation. 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.
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Security & GovernanceThe Chronicles of AI Ethics: The Man, The Machine, And The Black Box
Today, machine learning and artificial intelligence systems, trained by data, have become so effective that many of the largest and most well-respected companies in the world use them… Aparna Dhinakaran March 12, 2021 6 min read -
AI ObservabilityThe Playbook to Monitor Your Model’s Performance in Production
As Machine Learning infrastructure has matured, the need for model monitoring has surged. Unfortunately this growing demand has not led to a foolproof playbook that explains to teams… Aparna Dhinakaran March 4, 2021 11 min read -
AI ObservabilityThe Model’s Shipped; What Could Possibly go Wrong?
In our last post we took a broad look at model observability and the role it serves in the machine learning workflow. In particular, we discussed the promise… Aparna Dhinakaran February 22, 2021 8 min read -
AI ObservabilityML Infrastructure Tools — ML Observability
The ML Observability platform allows teams to analyze model degradation and to root cause any issues that arise. This ability to diagnose the root cause of a model’s… Aparna Dhinakaran February 3, 2021 4 min read -
Security & GovernanceWomen In AI To Watch
It’s been a strange year, to say the least. Despite the total chaos, it’s a critical and exhilarating time to work in Artificial Intelligence. Whether you want to… Aparna Dhinakaran November 17, 2020 12 min read -
AI ObservabilityUsing Statistical Distance Metrics for Machine Learning Observability
Statistical Distances are used to quantify the distance between two distributions and are extremely useful in ML observability. This blog post will go into statistical distance measures and… Aparna Dhinakaran October 19, 2020 10 min read -
AI ObservabilityArize AI Named TiE50 Award Winner at TiEcon
Arize AI, the first-to-market ML Observability Platform, is excited to announce that it has been selected as a 2020 TiE50 Winner in the prestigious TiE50 Awards Program. This… Aparna Dhinakaran August 31, 2020 3 min read -
AI ObservabilityML Infrastructure Tools for Production (Part 1)
Machine Learning (ML) is being adopted by businesses in almost every industry. Many businesses are looking towards ML Infrastructure platforms to propel their movement of leveraging AI in… Aparna Dhinakaran August 5, 2020 9 min read -
AI Observability
ML Infrastructure Tools for Model Building
Artificial Intelligence (AI) and Machine Learning (ML) are being adopted by businesses in almost every industry. Many businesses are looking towards ML Infrastructure platforms to propel their movement… Aparna Dhinakaran May 14, 2020 8 min read -
AI Observability
ML Infrastructure Tools for Data Preparation
Artificial Intelligence (AI) and Machine Learning (ML) are being adopted by businesses in almost every industry. Many businesses are looking towards ML Infrastructure platforms to propel their movement… Aparna Dhinakaran May 14, 2020 7 min read
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Arize gives AI teams observability and evals to understand and improve agent performance.
