Eugene Yan has published a guide on writing design docs for machine learning systems, aimed at teams that want to think through a project before diving into code. The post focuses on two main areas: the content of the design doc itself and the review process that follows.
Yan suggests that a good ML design doc should cover both methodology and implementation. That means being explicit about the problem, the data, the modeling approach, and how the system will be built and evaluated. The goal is to surface assumptions and tradeoffs early, before they become costly to change.
The review process is treated as a key part of the exercise. Yan argues that design docs are not just paperwork; they are a tool for getting feedback, aligning expectations, and building shared understanding among engineers, product managers, and other stakeholders. The post offers practical pointers for making that review useful rather than ceremonial.
Since this is a single source, there are no differing viewpoints to compare. The value of the piece is in its concrete, experience-based advice for teams that want to reduce risk in ML projects by writing things down first.