The discussion with Chip will cover these topics -

  1. Common abstractions for MLOps workflows

  2. Unifying production & dev environments for ML engineers & data scientists

  3. Iteration speed for ML models at different companies

  4. Real-time ML

  5. ML on the edge

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Instructor's Bio

Chip Huyen

Adjunct Lecturer | Founder, Stanford University

Chip Huyen is an engineer and founder working to develop tools that leverage real-time machine learning. Through her work with Snorkel AI, NVIDIA, and Netflix, she has helped some of the world’s largest organizations deploy machine learning systems. She teaches Machine Learning Systems Design at Stanford. She’s also published four bestselling Vietnamese books.

Ville has been developing infrastructure for machine learning for over two decades. He has worked as an ML researcher in academia and as a leader at a number of companies, including Netflix where he led the ML infrastructure team that created Metaflow, a popular open-source framework for data science infrastructure. He is the co-founder and CEO of Outerbounds, a company developing modern human-centric ML. He is also the author of an upcoming book, Effective Data Science Infrastructure, published by Manning.


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    ON-DEMAND WEBINAR:Rise of MLOps and Data Engineering

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