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Risk register

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

A risk register is a maintained record of identified risks, their characteristics, and actions for addressing them.

A risk register is a maintained record of what might go wrong, what remains unknown, and what the team may need to do about it. Establish it at the start of the project alongside a shared document repository. Its purpose is to make uncertainty visible before it becomes an unplanned delivery cost. section 2.2

Add a risk when an assumption needs checking or a delivery constraint may change the plan:

  • Record unanswered questions about data access, contents, and quality. Where a contract is involved, put consequential assumptions in the statement of work; insufficient data knowledge may also require time and funding contingency. section 2.4
  • Record potential security or privacy obstacles even when the resolution is not yet known. Determine whether they require a contractual assumption or affect project viability and cost. section 2.5
  • Record gaps between promised and available infrastructure, or a resourcing plan that can no longer be staffed. Work through mitigations such as a workaround, a scope change, replanning, or replacement resources. section 4.4.2 section 4.2

Review the register when preparing the resource estimate. Identify possible mitigations or workarounds, estimate their cost and confidence, and allow for that uncertainty in the budget. Check especially for high-impact risks with no clear path to resolution. Brief an accountable officer, obtain sign-off on the proposed budget and risk, and document the decision. During delivery, a weekly stakeholder review can keep the register current. section 3.2.3

Pricing an uncertain mitigation

If a mitigation is estimated at $500, a low-confidence estimate with a 100% risk premium becomes $1,000 in the project budget. A high-confidence mitigation with a 30% premium becomes $650. These figures make the cost of uncertainty explicit rather than leaving it outside the estimate. section 3.2.3

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

How deep it goes
Gives setup, risk-entry situations, mitigation pricing, sign-off, and review guidance without prescribing a register template.
Examples
Uses inaccessible data, delayed GPU infrastructure, staffing shortfalls, and $500 mitigation estimates as concrete risk cases.
What it assumes
Assumes a project with stakeholders and resource estimates; introduces the register as a day-one project-management task.
What you can do after
Practice/perform risk reviews, budget for mitigations, document sign-off, and update the register with stakeholders.
How it connects
Connects data readiness and contractual assumptions to project estimates and risk mitigation.

Related concepts

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.