Monitoring AI in Production
AI systems change after launch as data drifts and vendors update models. Know what to watch, when to act, and who has the authority to switch a system off.
What you'll be able to do
- Choose monitoring signals across outcomes, fairness, use and cost
- Set thresholds tied to your testing and name who responds
- Keep the logs and records you'll need for an investigation
- Define AI incidents and meet reporting deadlines when they happen
An AI monitoring and incident plan
What's in it. Signals across outcomes, inputs, outputs, fairness, use, cost and vendor changes, thresholds tied to testing, owners and response steps with switch-off authority, logging and retention, incident criteria and reporting deadlines, and the review cycle.
You build it lesson by lesson, using your own organization, and submit it for your certificate. It's yours to keep and adapt.
Who it's for
ML operations, AI governance, risk and product teams running AI in production.
What's inside
- 1Where the requirement comes from 18 min
- 2How small teams and enterprises meet it 16 min
- 3What to monitor 24 min
- 4Thresholds, owners and response 22 min
- 5Logs, records and vendors 20 min
- 6Incidents and reporting 22 min
- 7Hands-on lab: small team choose one 22 min
- 8Hands-on lab: enterprise choose one 24 min
- 9Finish and submit your deliverable 20 min
Built for your size
The requirement is the same everywhere. How you meet it depends on who you have. You pick the lab that matches your organization.
How you earn the certificate
Submit your finished deliverable, which is scored against a published rubric, and pass a short scenario quiz. Your certificate goes to your wallet and can be checked by anyone on the public register.