A guide to optimizing ML service latency and ML inference latency


A primer on the different levels of explainability and how each can be used across the ML lifecycle


Alex Zamoshchin, Image by Author


Notes from Industry

Many companies are learning that bringing a model that works in the research lab into production is much easier said than done.

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Notes from Industry

Image by Author


New perspectives on an ever-evolving role

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On defining, measuring, and fixing drift in your machine learning models

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Notes from Industry

Building machine learning proof of concepts in the lab is drastically different from making models that work in the real world.



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Aparna Dhinakaran

Co-Founder and CPO of Arize AI. Formerly Computer Vision PhD at Cornell, Uber Machine Learning, UC Berkeley AI Research.

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