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MLOps and AI infrastructure (year 2)

The next step after DevSecOps: how models are trained, tracked, packaged, served and watched in production, on Kubernetes.

Course by
Open courses
Course code
MLOps
Version
2026
Lessons
30

These summaries follow the public outline of the course. I wrote every lesson in my own words and tested the commands on RHEL 10. They are not Red Hat training material. Take the official course for the full content and the hands-on labs.

Parts and chapters

Part 1

Concepts

  1. 1Learn the machine-learning words an engineer needsMachine learning basics4 lessons
    Lessons
    1. 1.1 Know what a model is
    2. 1.2 Know the main kinds of ML
    3. 1.3 Train a tiny model
    4. 1.4 Understand why ML needs operations
  2. 2Follow the ML lifecycleThe MLOps lifecycle4 lessons
    Lessons
    1. 2.1 See the lifecycle
    2. 2.2 Know the maturity levels
    3. 2.3 Version everything
    4. 2.4 Add DevSecOps to ML
  3. 3Track experiments and models with MLflowExperiment tracking and model registry4 lessons
    Lessons
    1. 3.1 Run an MLflow server
    2. 3.2 Log a training run
    3. 3.3 Compare runs
    4. 3.4 Promote models in the registry

Part 2

Pipelines and serving

  1. 4Automate training with pipelinesOrchestration and training pipelines4 lessons
    Lessons
    1. 4.1 Split training into steps
    2. 4.2 Choose an orchestrator
    3. 4.3 Write a Prefect flow
    4. 4.4 Add quality gates
  2. 5Serve models as APIsModel deployment and serving5 lessons
    Lessons
    1. 5.1 Build a model API
    2. 5.2 Package it in a container
    3. 5.3 Choose online or batch
    4. 5.4 Serve on Kubernetes with KServe
    5. 5.5 Release models safely
  3. 6Monitor models in productionModel monitoring4 lessons
    Lessons
    1. 6.1 Watch the service
    2. 6.2 Detect data drift
    3. 6.3 Measure real quality
    4. 6.4 Close the loop
  4. 7Run AI workloads on KubernetesML platforms on Kubernetes5 lessons
    Lessons
    1. 7.1 Give pods GPUs
    2. 7.2 Know Kubeflow
    3. 7.3 Share the cluster fairly
    4. 7.4 Secure the ML platform
    5. 7.5 Plan your first MLOps project