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How to become a MLOps Engineer

Make machine learning reliable in production: pipelines, deployment, monitoring and retraining.

22-33 weeks4 phases17 steps18 free resources4 checkpoint tests

Before you start this one

Around 1-3 years in DevOps, backend or ML. MLOps sits on top of both, so it is a second role rather than a first one.

Best for

Engineers who like infrastructure and are comfortable with models being probabilistic rather than deterministic.

Maybe not for you if

Freshers with neither ML nor operations experience. Enter through data engineering, DevOps or ML first.

The 4 phases

What each phase gets you to. The steps, resources and checkpoint inside each one open with a free account.

  1. Phase 1 - Engineering foundations: containers, CI/CD and Terraform

    6-9 weeks

    Goal: Be genuinely competent with Linux, Docker, GitHub Actions and Terraform on one cloud, because every ML tool later assumes all four.

    5 steps · ends in a scored checkpoint (70% to clear) · assumes some devops, backend or ml experience, can write working python without a tutorial open

  2. Phase 2 - The ML lifecycle: evaluation, tracking and data versioning

    5-8 weeks

    Goal: Understand enough modelling, tracking and data versioning to operate someone else's models responsibly.

    3 steps · ends in a scored checkpoint (65% to clear)

  3. Phase 3 - Deploy, serve and monitor

    7-10 weeks

    Goal: Serve a model reliably, make it visible in production, and detect when it starts going wrong.

    6 steps · ends in a scored checkpoint (70% to clear)

  4. Phase 4 - Automate retraining and close the loop

    4-6 weeks

    Goal: Close the loop so the system improves on a schedule, and only when the new model is genuinely better.

    3 steps · ends in a scored checkpoint (70% to clear)

Open the full MLOps Engineer roadmap

Free account, no card. It takes about a minute and you do not need to verify your email to start.

  • All 17 steps, in order, with why each one is there
  • 18 hand-picked free resources, no paid course upsells
  • The specific mistake people make at each stage
  • 4 scored checkpoint tests, so progress is earned not ticked
  • Progress saved per step, so a break does not cost you the thread
  • Adaptive start, phases your test history already clears are skipped
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What hiring actually looks like here

MLOps is commonly described as a transition role from DevOps or data engineering rather than an entry point, and typically expects cloud, containers, CI/CD and infrastructure-as-code alongside ML familiarity. Postings in 2026 increasingly add LLM serving on top of that base, but the base is still what usually gets screened first.

We claim no placement outcome, salary or success rate. This describes what is commonly reported about the role, nothing more. Linked resources are credited to their authors.

Comparing paths?