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Governance

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

System governance is the set of decision rights, controls, responsibilities, and processes that direct and oversee a system throughout its lifecycle.

Governance assigns accountability, controls, review processes, and decision rights for managing a system and its effects throughout its lifecycle. It keeps responsibility explicit after delivery, even when team members change. section 9.7 section 10.2.1

Establish governance as an operational work item:

  • Name the owner and the person who manages each system; obtain agreement from the accountable manager. section 10.2.1
  • Define effective controls, user access to those controls, and a responsible authority for consequential judgments. section 9.7
  • Review performance through effects on users and others, consult impacted people, and measure harm against utility. section 9.7 section 6.2
  • Check support, documentation, tools, infrastructure changes, failure handling, end-of-life risks, and technical debt. section 10.2.1
  • Record inadequate governance or harmful impacts in the risk register and address them through the project backlog. section 6.2
  • Repeat reviews on an agreed schedule, beginning with an annual review when no organizational interval exists. section 10.2.1

Production handoff

During The Bike Shop deployment, support groups were prepared, a governance process was assigned, logging and monitoring were connected to the model server and data subsystem, and testing preceded release. section 9.10

Table 10.2 Create and maintain a list of your responsibilities, ensure that your manager or supervisor has access to it, and go over it with them to make sure they agree that it’s accurate. (view table figure)

System name

Owner

Who manages

Notes

Inventory manager

Karima

You

Reviewed and good 03/22

Demand prediction

Karima

You

Reviewed and good 03/33

Building manager

Alan

David

David isn’t sure about this

Auto reply

Alan

You

No governance

Inspection

Josep

David

David agreed

Entity matcher

Alan

You

No governance

Auto review

Alan

You

Last reviewed 01/20

Use the responsibility table to expose missing ownership, unresolved management assignments, review status, and systems with no governance. section 10.2.1

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

How deep it goes
Defines owners, controls, reviews, impact measures, and remediation, but defers a fully standardized ethical evaluation methodology.
Examples
Uses responsibility tables, stakeholder impact assessments, risk-register entries, production monitoring, and The Bike Shop handoff.
What it assumes
Assumes a deployed or developing machine-learning system, stakeholders, accountable managers, and an operational context; teaches governance checks from practical questions.
What you can do after
Operate/configure system ownership, controls, reviews, monitoring, support, and remediation across the production lifecycle.
How it connects
Connects ethics, risk registers, project backlogs, monitoring, support organizations, documentation, failure handling, and lifecycle management.

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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.