Principles of Good Machine Learning Systems Design

Explore the principles of effective machine learning systems design in this 36-minute conference talk by Chip Huyen, ML Engineer and Open Source Lead at Snorkel AI, presented at the Toronto Machine Learning Series. Delve into the distinctions between ML in research and production environments, and understand the unique challenges of ML systems compared to traditional software. Examine common myths surrounding ML production and learn an iterative framework for designing ML systems, covering project scoping, data management, model development, deployment, maintenance, and business analysis. Gain insights into the roles of DataOps, ML Engineering, MLOps, and data science within this framework, and discover the key skills required at each stage to help structure effective teams. Conclude with an overview of the ML production ecosystem, exploring the economics of open source and open-core businesses.

Principles of Good Machine Learning Systems Design