Section 046 · Chapter 7, Release Readiness for AI Systems
Monitoring After Release
For non-deterministic systems, launch is not the end of testing. It is the start of real-world measurement.
monitoringmonitoring after release
What to do
- Build an evaluation loop that notices when reality changes.
- Version every evaluator and baseline.
- Define runnable checks that exercise monitoring and monitoring after release.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for monitoring, monitoring after release needed to reproduce work on Monitoring After Release.
- Report results for monitoring, monitoring after release by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
Expert monitoring separates data drift, model drift, behavior drift, and evaluation drift. If the judge changes, apparent product quality can change even when the product did not. Version every evaluator and baseline.
Continue the conversation
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Save your product context once, then open a focused conversation that combines it with this concept.
Cite this page
Jason Arbon. "Monitoring After Release." Testing AI Knowledge Edition, section 46.
https://jarbon.ai/testing-ai/knowledge/ch046-monitoring-after-release.html