Introduction to AI operations
Operate AI as a measurable service whose technical health, behavior, risk, and cost can change independently.
Before you start
Module prerequisites: SAI-100. Check the exercise-specific tools and environment before running commands.
Module outcomes: Define useful AI service indicators; Connect evaluation signals to operations; Measure capacity, unit cost, and accepted outcomes. This lesson contributes to that sequence.
Operate technical health and behavior together
An AI endpoint can be fast and available while returning unsupported, unauthorized, or unusable results. It can also provide good results while exhausting scarce capacity or losing required evidence. AI operations connects platform health, service behavior, policy, evaluation quality, user outcome, risk, and cost.
Operating questions
- What does a successful request mean for this workload?
- Which versions and dependencies produced the outcome?
- How are degradation, unsafe behavior, and evidence gaps detected?
- What capacity is available under the real request mix?
- Which cost unit supports a useful decision?
- When should release, rollback, incident, or reassessment processes begin?
Principles
Measure distributions rather than averages, preserve context for comparisons, protect sensitive telemetry, and separate indicators from objectives. Connect change events to observed behavior. Track accepted outcomes rather than optimizing token volume or utilization alone.
Module outcome
You will create an indicator and objective catalog, telemetry map, incident and recovery playbook, capacity and unit-economics model, and operational evidence report.
Module worked example
A service reports low token cost while many requests miss their latency target.
- Apply the control
- Calculate cost per accepted outcome and inspect errors, queue time and tail latency under the same load.
- Expected result
- The operating scorecard connects cost and capacity decisions to useful service outcomes.
- Troubleshooting
- Include timed-out and rejected work in the denominator rather than reporting only successful responses.
Practice and completion check
Choose one control from this lesson. Identify its actor, target, enforcement point and expected result. Describe an allowed case and a denied or failed case. For a runnable exercise, retain observed output and separate it from expected output. Record unresolved limitations before marking the lesson complete.
Your lesson progress
Completion is self-reported learning progress; it does not certify practical competence.
Status: Not marked
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