Project Planning

Project Planning translates the business understanding, feasibility assessment, and risk analysis into an actionable roadmap. ML projects require special planning considerations due to their iterative nature, experimentation cycles, and uncertainty in outcomes.

Unlike traditional software projects, ML planning must accommodate the experimental nature of model development where the exact path to success is often unclear. The plan should be flexible enough to adapt to discoveries during development while maintaining clear milestones and accountability.

This process produces the Project Charter and Work Breakdown Structure that guide execution across all MAISTRO phases.

A comprehensive ML project plan addresses six key components:

While every project varies, here's a typical timeline structure for a medium-complexity ML project following the MAISTRO framework:

ML project estimation is notoriously difficult due to uncertainty. Use these guidelines as starting points and adjust based on complexity:

Define clear responsibilities using a RACI matrix. Here's a typical example for ML projects:

• 1
  Work Breakdown Structure (WBS)
  Decompose the project into manageable tasks and subtasks. Identify dependencies between tasks and organize into a hierarchical structure.
• 2
  Effort Estimation
  Estimate effort for each task using techniques like expert judgment, analogous estimation, or three-point estimation. Add buffers for uncertainty.
• 3
  Resource Allocation
  Match tasks to team members based on skills and availability. Identify resource gaps and plan for hiring, training, or external support.
• 4
  Schedule Development
  Create the project schedule with milestones, gates, and deliverables. Use critical path analysis to identify schedule risks.
• 5
  Budget Finalization
  Consolidate cost estimates and create the project budget. Include contingency reserves and get budget approval from sponsors.
• 6
  Project Charter Creation
  Document all planning elements in a formal Project Charter. Get sign-off from key stakeholders before moving to execution.

ML projects benefit from hybrid approaches that combine the structure of traditional planning with the flexibility of agile methods:

• ✓
  Build in Experimentation Time
  ML projects require exploration. Explicitly allocate time for experiments that may not succeed but inform the path forward.
• ✓
  Plan for Iteration
  Expect to cycle back through phases as you learn. Build this into the schedule rather than treating it as a surprise.
• ✓
  Define Clear Gates
  Establish clear criteria for moving between phases. This prevents premature advancement and ensures quality.
• ✓
  Include Contingency
  Add 20-30% buffer to estimates for ML projects. The uncertainty is higher than traditional software development.
• ✓
  Communicate Uncertainty
  Be transparent with stakeholders about estimation uncertainty. Range estimates are more honest than single-point estimates.