This talk is part of the AI+Data Summit Europe 2020 and it 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. The talk ends with a survey of the ML production ecosystem, the economics of open source, and open-core businesses.
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.
1:32 Outline
2:11 When to use machine learning
7:13 ML in research vs. in production
14:58 ML production myths
23:05 Iterative process
