LLMOps: Evaluation, Observability and AI Security

About this course

Getting an LLM feature to demo quality is easy; keeping it correct, safe, fast and affordable in production is the real work. This course covers the LLMOps lifecycle end to end: offline and online evaluation with calibrated LLM judges, release gates for prompts and models, tracing and quality monitoring with open standards, the LLM attack surface and red teaming, cost and latency engineering, and incident response and governance for AI-specific failures. Learners leave able to own the operational risk of an LLM service.

For: ml practitioner, software engineer, architect, tech leader.

Learning outcomes

On successful completion you will be able to:

1. 1Explain the LLMOps lifecycle (evaluation, release, observability, security and incident response) and how it differs from classical MLOps and DevOps.
2. 2Design evaluation suites that combine golden datasets, programmatic checks, LLM-as-judge scoring and online signals, and wire them into release gates.
3. 3Instrument an LLM application with traces, metrics and feedback capture using open standards, and build dashboards and alerts for quality, cost and latency.
4. 4Assess an LLM application's attack surface, red-team it against prompt injection, data leakage and jailbreaks, and prioritise defences.
5. 5Analyse and reduce the cost and latency of an LLM application through caching, batching, routing and context management without degrading measured quality.
6. 6Own the operational risk of an LLM service - define SLOs, lead the response to AI-specific incidents and decide when to roll back or disable a feature.

Modules and lessons

1. Week 1Evaluation foundations⌄

   • The LLMOps lifecycle
   • Offline evaluation and golden datasetslab
2. Week 2Releasing with confidence⌄

   • Release gates for prompts and models
   • Online evaluation and feedbacklab
3. Week 3Observability⌄

   • Tracing and telemetry for LLM applicationslab
   • Monitoring quality, drift and SLOs
4. Week 4AI security and red teaming⌄

   • The LLM attack surface
   • Red teaming and guardrailslab
5. Week 5Cost and latency engineering⌄

   • Cost engineering for LLM applications
   • Latency engineeringlab
6. Week 6Operating AI services⌄

   • Incident response for AI failures
   • Governance of LLM operations

Assessment

• Quiz (15%)

  Six weekly quizzes drawn from the lesson quiz banks; best five count.

• Lab (30%)

  Five graded labs covering judge calibration, online experiments, tracing, red teaming and latency reduction.

• Project (35%)

  Take a provided LLM application to production readiness - an evaluation suite with a CI gate, observability, a red-team report and the fixes it led to.

• Viva (20%)

  Incident simulation in which the learner leads the response to a live AI failure scenario and justifies each decision.

Pass mark 60%. Grades: Distinction, Merit, Pass.