How to Write Design Docs for Machine Learning Systems

A practical guide to writing design documents for machine learning systems, structured around the Why, What, and How framework. Covers what to include in the methodology section (problem framing, data, techniques, validation, human-in-the-loop) and the implementation section (high-level design, infra, scalability, performance, security, data privacy, monitoring, cost, integration points, risks). Also explains a two-step review process: an informal pre-review with a small group for early feedback, followed by a formal review with senior stakeholders. Includes a minimal open-source template and guidance on when writing a design doc is and isn't worth the overhead.