MLOps learning roadmap

Turn a validated model into a tested, deployable, observable production system with a safe path for continuous improvement.

5 modules About 74 hoursFree curated resources

Complete Data Engineering + Data Science first

What you will learn

  1. 01

    Experiments & Reproducible Pipelines

    Track experiments, version artifacts, orchestrate training, and package the same workflow for local and automated execution.

    Experiment tracking · Pipeline orchestration · Reproducible containers

    14h
  2. 02

    CI/CD for ML

    Gate every change with code, data, model, integration, security, and artifact checks before a release can move forward.

    ML quality gates · Immutable artifacts · Release automation

    14h
  3. 03

    Serving & Safe Release

    Implement batch and online inference, deployment health checks, progressive delivery, load evidence, and executable rollback.

    Batch & online serving · Progressive delivery · Rollback engineering

    14h
  4. 04

    Monitoring & Incident Response

    Observe service, data, model, and business behavior; connect alerts to ownership, diagnosis, rollback, and retraining decisions.

    Four-layer monitoring · Drift & delayed labels · Incident response

    14h
  5. 05

    Continuous ML Platform Capstone

    Complete a production-grade continuous-training system with governed promotion, infrastructure, observability, recovery, and evidence.

    Continuous training · Governed promotion · Platform ownership

    18h