Monitor live AI systems for output quality and act on leading indicators
AI for Business Institute · D9
Performance & Continuous Improvement
Domain 9 of the AI for Business Institute practitioner standard. Monitor live AI systems for output quality and act on leading indicators; enforce an evaluation and testing discipline built on golden datasets and regression; run an improvement and release process proven by exercised rollback and shipped changes; establish post-deployment ownership and a post-implementation review that tests the business case; and run corrective action that knows when to decommission.
Not sure where to begin? Take the free 18-question mock testDomainD9
Time3.4 hours
Structure5 modules · 20 lessons
What you get
$250, and exactly what that buys.
- 5 modules, 20 lessons
One module per framework criterion, about 3.4 hours of material.
- Applied practice, not just reading
Applied scenarios, Card sorting, Matching exercises and more.
- Work at your own pace
Progress is saved as you go, so you can stop and resume on any device.
- Access that does not expire
Once bought, the course stays in your account. There is no renewal to keep it.
- Counts toward the full programme
Complete all nine domains and the Institute issues your programme completion certificate.
This does not include the accreditation exam. This course builds and evidences capability. Accreditation is a separate assessment, available with the full programme for $2,000, and the credential is awarded only if you pass it.
Why this course
Know what good looks like. Practise the decisions that make it real.
Domain 9 of the AI for Business Institute practitioner standard. Monitor live AI systems for output quality and act on leading indicators; enforce an evaluation and testing discipline built on golden datasets and regression; run an improvement and release process proven by exercised rollback and shipped changes; establish post-deployment ownership and a post-implementation review that tests the business case; and run corrective action that knows when to decommission.
Capabilities
What you'll be able to do
enforce an evaluation and testing discipline built on golden datasets and regression
run an improvement and release process proven by exercised rollback and shipped changes
establish post-deployment ownership and a post-implementation review that tests the business case
and run corrective action that knows when to decommission.
How you will learn
Learn. Practice. Prove.
Learn
Short, evidence-led lessons establish the standard and the reasoning behind it.
Practice
Interactive decisions, sorting and scenarios turn concepts into applied judgement.
Prove
A cumulative final exam checks mastery, followed by a spoken capstone.
Inside this courseApplied scenariosCard sortingMatching exercisesPriority rankingConfidence checksReflection journal
Curriculum
A concise view of the complete course.
Performance monitoring and KPIs
Why monitoring a live AI system must cover output quality rather than availability alone, which signals warn earliest, and what a monitoring KPI set is actually for.
Evaluation and testing discipline
Why a change to a live generative system must pass an evaluation suite before production, what a robust pre-deployment testing discipline looks like, and why a golden dataset and regression testing are the backbone of both.
Improvement cycles and release management
What evidences a functioning improvement and release process, why rollback exercised in practice matters more than rollback documented, and how to respond to gradual performance decline through an established process.
Post-implementation review and lessons learned
Why the handover from project to operation is where AI systems fail, what post-deployment ownership must establish, and what a post-implementation review of an AI system should include.
Corrective action, remediation and decommissioning
How a mature corrective action process responds to persistent underperformance, why the decision to decommission is a value judgement taken against defined criteria, and how to run corrective action before the decommission question arises.
Ready when you are