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Acceptance criteria

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

Acceptance criteria are conditions that a delivered system or project result must satisfy to be accepted.

Acceptance criteria identify requirements and conditions that a project result must satisfy before delivery is accepted. During project validation, derive candidate criteria from stakeholder user stories rather than treating them as an isolated checklist. section 3.1

Use stakeholder stories to expose criteria that reduce delivery risk:

  • Document key functionality and requirements for business sponsors, employees or users, customers, and affected persons. section 3.1
  • Check first use, normal use, and system exit to reveal requirements, harms to avoid, and corner cases. section 3.1
  • Describe which ML model each stakeholder invokes, what it does, and what data it needs to create and run. section 3.1

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

How deep it goes
Identifies candidate criteria through stakeholder scenarios but defers fleshing them out in detail until funding and project resources exist.
Examples
Uses first use, normal use, and system exit across sponsors, users, customers, and affected persons as requirement-revealing scenarios.
What it assumes
Builds from stakeholder identification and user stories, while requiring attention to available data, team skills, and implementation feasibility.
What you can do after
Evaluate candidate requirements by translating stakeholder stories into functionality, avoided harms, corner cases, model behavior, and data needs.
How it connects
Connects acceptance criteria with user stories, requirements, stakeholders, corner cases, harms, ML models, and data requirements.

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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.