How to Write Design Docs for Machine Learning Systems
Eugene Yan
This article provides guidance on writing design documents for machine learning systems, structured around Why, What, and How frameworks. It covers problem definition, success criteria, methodology including data and techniques, and implementation details like infrastructure and monitoring, emphasizing that design docs help clarify thinking and prevent costly late-stage project failures. The article includes practical checklists and templates for considering requirements, constraints, validation approaches, and operational considerations when designing ML systems.
Why it matters
Pointers to think through your methodology and implementation, and the review process.
