Скачать или смотреть Principles of Good Machine Learning Systems Design

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.

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

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