Academy / Generative AI Engineering

Path 3 · Course 4 · For developers, ML engineers and platform teams

Evaluating & Deploying AI (LLMOps)

Take generative AI from prototype to production: evaluation suites and release gates, guardrails, deployment architecture on APIs and cloud platforms, monitoring and tracing, and managing cost, latency and model change.

📘 6 modules⏱ About 3.5 hours✅ Knowledge check per module🎓 Final assessment & certificate🎯 Python; LLM app basics

0 of 6 modules complete

Start Module 1 Final assessment

Modules

  1. 1

    From prototype to production

    The prototype trap, LLMOps vs MLOps, the anatomy of a production LLM application, and the LLMOps lifecycle.

    25 min
  2. 2

    Evaluating LLM quality

    Success criteria, evaluation sets, code / LLM / human graders, an evaluation harness in Python, evals as release gates, and offline vs online evaluation.

    35 min
  3. 3

    Guardrails and safety in production

    Layered guardrails, PII redaction, content moderation, structured output validation, graceful failure and safety test suites.

    30 min
  4. 4

    Deploying AI applications

    Provider APIs vs cloud AI platforms vs self-hosting, gateways, retries and fallbacks in code, latency, scaling and rate limits, security and data residency.

    35 min
  5. 5

    Monitoring and observability

    What to observe, tracing, structured logging in Python, online quality monitoring, drift, alerts and incidents, and privacy in logs.

    30 min
  6. 6

    Managing cost, change and governance

    Versioning everything, safe release techniques, model upgrades and deprecations, cost controls in code, operational governance and a readiness checklist.

    30 min