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Estimate

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

Estimation is the process of forecasting the effort, resources, schedule, or cost needed to deliver a project or its results.

Project estimation transforms a defined hypothesis into a credible envelope of resources, money, people, and time for delivering a viable result. It accounts for team capacity, work scale, delivery commitments, and known risks while recognizing that plans will adapt as the project unfolds. section 3.2 section 3.7

Build an estimate through explicit project artifacts and decision points:

  • Define the work required to deliver the project hypothesis, including its scale and expected outcome. section 3.2
  • Create and socialize the team design and resourcing plan. section 3.2
  • Review the risk register and price mitigation, resolution effort, uncertainty, and required elasticity into the budget. section 3.2.3
  • Review the completed pre-sales process, document assumptions, and obtain accountable sign-off before discussing customer costs. section 3.2 section 3.7

Risk-adjusted mitigation

A $500 mitigation with low confidence is budgeted at $1,000; a high-confidence mitigation is budgeted at $650, before organizational margin. High-impact risks without credible mitigations require explicit escalation and acceptance. section 3.2.3

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

How deep it goes
Specifies work, resourcing, risk premiums, review, documentation, and sign-off without detailing task-level forecasting methods.
Examples
Uses estimate-building tickets, risk registers, mitigation premiums, review meetings, documentation, and accountable budget sign-off.
What it assumes
Assumes a defined project hypothesis, requirements, delivery environment, system outline, and documented legal or ethical review.
What you can do after
Apply a structured estimate that exposes resource needs, uncertainty, mitigation costs, and organizational risk before commitment.
How it connects
Connects project requirements, project hypotheses, team design, resourcing, risk registers, budgets, and project proposals.

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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.