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Continual Learning for Machine Learning in Production

Because of data drift, the accuracy of an ML model degrades over time. How well a model performs depends on how often it’s updated. While companies like Alibaba, ByteDance, Google, Facebook have been able to leverage real-time pipelines to continually update many of their models in production and boost their performance, many companies still update their models manually. In this talk, we’ll discuss the state of continual learning for ML, its motivations, challenges, and possible solutions.


Chip Huyen

Founder at stealth startup & Teaching ML Sys @Stanford

Chip Huyen is an engineer and founder working to develop tools for ML models to continually learn in production. 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...

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Tuesday Nov 9 / 10:10AM PST (40 minutes)


ML Everywhere


Machine LearningData EngineeringArtificial Intelligence

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