Product owner

Q: How deeply does Delivering Machine Learning Projects: From design to deployment cover product owner?

Describes staffing choices around stakeholder investigation, but does not specify a procedure for gathering feedback or setting priorities.

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

A product owner is a customer representative who provides feedback and priorities so a project responds to customer needs.

A product owner is a client representative who brings customer feedback and priorities into an ML project. The role helps the team keep its work connected to user needs and business context. section 3.2.2

Decide how much investigation the product owner can realistically do. A client acting as product owner may work with users and stakeholders to develop insights for the team leader and help technical contributors understand business data. If detailed investigation would consume too much of the owner’s time, assign it to a business translator. section 3.2.2

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

Cover of Delivering Machine Learning Projects: From design to deployment

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Related concepts

roadmapconfigures · A roadmap is a time-oriented planning artifact that communicates intended goals, initiatives, milestones, capabilities, or changes and their sequence.
scenariouses · A scenario is a concrete description of a situation or conditions, often including actions and expected or possible outcomes, for analysis or evaluation.
living documentationuses · Living documentation is documentation maintained or validated as a system evolves so that it remains aligned with the system, behavior, design, or decisions it describes.
collaborative discovery and modelingused by · Collaborative discovery and modeling is a group technique in which stakeholders jointly create and refine representations to establish shared understanding of a domain or system.
backlogconfigures · A backlog is a collection of pending work items or orders awaiting prioritization, processing, fulfillment, or completion.

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.