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User story

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

A user story is a concise description of a system capability from the perspective of a person or group affected by or using the system.

A user story describes a system capability from the perspective of a stakeholder who uses or is affected by it. During project validation, it connects business opportunities to viable implementation paths. section 3.1

Develop user stories to:

  • Identify functionality and requirements. section 3.1
  • Suggest acceptance criteria, harms to avoid, and corner cases that could make the system irrelevant or unworkable. section 3.1
  • Cover sponsors, employees or users, customers, and other affected persons across first use, normal use, and discontinuation. section 3.1
  • Name the ML model invoked in each situation, its required training and runtime data, and its stakeholder-specific action. section 3.1

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

How deep it goes
Develops requirements, acceptance criteria, harms, corner cases, stakeholder situations, and model-data implications, while deferring detailed elaboration until execution.
Examples
Uses first use, normal use, and discontinuation scenarios for sponsors, users, customers, and affected persons.
What you can do after
Evaluate implementation viability and specify stakeholder needs, model behavior, data requirements, acceptance criteria, and risks.
How it connects
Connects functional requirements, systems stories, acceptance criteria, stakeholders, 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.