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Documentation plan

As explained in Delivering Machine Learning Projects: From design to deployment

A documentation plan is a documented arrangement specifying what project documentation will be created, maintained, and shared.

A documentation plan specifies which project documents will be created, maintained, shared, and delivered. It makes work visible and clarifies responsibilities, recipients, and timing. section 4.3.5

Build the plan around documentation deliverables such as:

  • Communication plan, privacy and security plan, infrastructure plan, technical architecture, and roadmap. section 4.3.5
  • Data survey, ethics report, business problem analysis, data test plan, EDA report, and model reports. section 4.3.5
  • Sprint reports, application and UI design, logging and monitoring approach, implementation report, testing report, and final project report. section 4.3.5

Sprint 0 approval checkpoint

During sprint 0, share the documentation plan with the client and confirm what will be delivered, to whom, and when. Adjust ownership when stakeholders approve individual sprints but senior management signs off on the whole project. section 4.3.5 section 4.3

How Delivering Machine Learning Projects: From design to deployment covers it

How deep it goes
Enumerates candidate deliverables and approval timing but stops short of defining each document's detailed content or maintenance procedure.
Examples
Uses ML project reports covering data, ethics, architecture, modeling, evaluation, implementation, testing, and final delivery.
What it assumes
Assumes a secured project team and an agreed working process, while establishing shared documentation and communication practices.
What you can do after
Operate a project with agreed documentation deliverables, recipients, timing, and review ownership.
How it connects
Connects documentation planning with communication plans, working processes, sprints, stakeholder review, and project sign-off.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Delivering Machine Learning Projects: From design to deployment. The section links point to the parts of the book it draws on.