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• Stanford MLSys Seminar Episode 5

Principles of Good Machine Learning Systems Design

• Episode 5 of the Stanford MLSys Seminar Series!
• Speaker: Chip Huyen

Speaker bio

Chip Huyen is an engineer who develops tools and best practices for machine learning production. She’s currently with Snorkel AI and she’ll be teaching Machine Learning Systems Design at Stanford from January 2021. Previously, she was with Netflix, NVIDIA, Primer. She’s also the author of four bestselling Vietnamese books.

Abstract

This talk covers what it means to operationalize ML models. It starts by analyzing the difference between ML in research vs. in production, ML systems vs. traditional software, as well as myths about ML production.

It then goes over the principles of good ML systems design and introduces an iterative framework for ML systems design, from scoping the project, data management, model development, deployment, maintenance, to business analysis. It covers the differences between DataOps, ML Engineering, MLOps, and data science, and where each fits into the framework. It also discusses the main skills each stage requires, which can help companies in structuring their teams.

The talk ends with a survey of the ML production ecosystem, the economics of open source, and open-core businesses.