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Roadmap

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

A project roadmap is a high-level, time-oriented plan that sequences project work, timing, dependencies, milestones, and major phases.

A project roadmap is the high-level plan that sequences tasks, timing, dependencies, and delivery phases. Build it after confirming the client context, data stores, team structure, and availability, then share it with the team and stakeholders. section 4.7

Organize the roadmap around three adjustable delivery phases:

  • Sprint 1: acquire data, build infrastructure, and begin UX work where early designs clarify nonfunctional model requirements. section 4.7
  • Sprint 2: build, evaluate, and select models. section 4.7
  • Sprint 3: integrate the system and prepare it for production. section 4.7
  • Move components forward or delay them when delivery circumstances change, but agree on the revised course with client stakeholders. section 4.7

Treat the roadmap as a governance and communication artifact, not only a scheduling document. Pair it with approved documentation deliverables that clarify what will be delivered, to whom, and when. section 4.3.5

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

How deep it goes
Explains three adjustable sprints and their dependencies, but stops short of prescribing detailed schedules or task estimates.
Examples
Uses three ML delivery sprints, early UX work, and documentation deliverables such as infrastructure, privacy, architecture, and evaluation reports.
What it assumes
Assumes a project hypothesis, presales estimates, delivery-context information, team availability, and access to client and data-store details.
What you can do after
Apply it by sequencing project work, revising phases when evidence changes, and securing stakeholder agreement on the revised plan.
How it connects
Connects data acquisition, UX design, nonfunctional requirements, model evaluation, system integration, productionization, and documentation.

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