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Career route 6

AI Evaluation and Reliability

For roles like AI Evaluation Engineer, AI Reliability Engineer, Machine Learning Site Reliability Engineer

Measure model and agent behavior, expose failure modes, instrument the runtime, and build release and incident controls around evidence.

12 lessons about 13 h of lessons Ends in a certificate Paid plans
Lesson checks passed0 of 12
Start: Model Evaluation

Is this route for you?

Do you want to turn model and system uncertainty into evaluation, observability, release, and recovery evidence?

  • You prefer measurable evidence to confident completion claims.
  • You enjoy finding edge cases, regressions, and system failure paths.
  • You want evaluation and operations to share the same release evidence.

What you'll be able to do: Build on shared foundations to define trustworthy behavior, expose failures, and operate AI releases through measurable reliability controls.

Start with

  • Complete the shared software engineering and AI foundations, or demonstrate equivalent working knowledge.
  • Understand model metrics, tests, APIs, and basic production telemetry.
  • Be able to reproduce failures and compare behavior across controlled runs.

The route

Common CoreDefine behavior, evaluation layers, failure modes, and trace semantics.
  1. 1Model EvaluationPhase 2: Machine learning fundamentals · 1.5 h
  2. 2Evaluation & Testing LLM ApplicationsPhase 11: LLM engineering and RAG · 45 min
  3. 3Eval-Driven Agent DevelopmentPhase 14: Agent engineering · 1 h
  4. 4Failure Modes: Why Agents BreakPhase 14: Agent engineering · 1 h
  5. 5OpenTelemetry GenAI Semantic ConventionsPhase 14: Agent engineering · 1 h

You produce: A layered evaluation plan with a failure taxonomy, trace schema, and regression thresholds.

Role PracticeMeasure inference health, production behavior, experiments, and capacity limits.
  1. 6Inference Metrics - TTFT, TPOT, ITL, Goodput, P99Phase 17: Infrastructure and production · 1 h
  2. 7LLM Observability Stack SelectionPhase 17: Infrastructure and production · 1 h
  3. 8A/B Testing LLM Features - GrowthBook, Statsig, and the Vibes ProblemPhase 17: Infrastructure and production · 1 h
  4. 9Load Testing LLM APIs - Why k6 and Locust LiePhase 17: Infrastructure and production · 1.5 h

You produce: An observability and load report that connects service metrics with behavior and user outcomes.

Proof ProjectProve release and recovery behavior under controlled failure.
  1. 10Shadow Traffic, Canary Rollout, and Progressive Deployment for LLMsPhase 17: Infrastructure and production · 1 h
  2. 11Chaos Engineering for LLM ProductionPhase 17: Infrastructure and production · 1 h

You produce: A regression and reliability gate with canary thresholds, rollback evidence, and a chaos result.

Interview and Readiness EvidenceShow how objectives, incidents, and corrective actions form an operating system for reliability.
  1. 12SRE for AI - Multi-Agent Incident Response, Runbooks, Predictive DetectionPhase 17: Infrastructure and production · 1 h

You produce: A reliability case study with SLOs, failure evidence, response decisions, and verified corrective actions.

Portfolio project: Regression and Reliability Gate

Build an evidence gate that connects behavior evaluation, runtime telemetry, load, rollout, and recovery decisions.

  • An evaluation set, rubric, and regression thresholds
  • A trace schema and observability view
  • An SLO and load test report
  • A canary and rollback decision record
  • A chaos test or incident analysis with corrective actions

You're ready when you can

  • Select metrics that match the behavior and failure risk being tested.
  • Build reproducible model, LLM, and agent regression checks.
  • Connect traces, inference metrics, and user-visible outcomes.
  • Define load, SLO, canary, and rollback thresholds.
  • Turn a failure exercise into a tested reliability improvement.

What this route covers, honestly

Covered in depth
  • Model, LLM, and agent evaluation
  • Failure taxonomy and observability
  • Inference metrics and load testing
  • Experiments, rollout, chaos, and SRE controls
Partly covered
  • Human annotation operations
  • Incident leadership
  • Large distributed infrastructure
Outside the course
  • Production on-call ownership
  • Compliance signoff
  • Large-scale benchmark laboratory operations

AI Evaluation and Reliability career route certificate

Pass all 12 lesson checks on this route (0 done), then the 20-question route exam drawn from these lessons. The certificate lists every topic you covered and carries an ID anyone can verify.

Exam requirements

Completing this route shows that you can build an evidence-based evaluation and reliability gate. It does not guarantee a role or prove experience owning live production incidents. This is a specialist overlay after shared foundations and combines adjacent evaluation and reliability work. It does not claim one universal job title or replace live incident and on-call experience. The estimate covers lesson time only. Portfolio work and practice take additional time.