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MLOPS

AI Engineering & MLOps

Promise

Become the person who puts AI into production — the best-paid skill on the market.

Courses and prices

  • AI Engineering & MLOps · Levels 100–200

    12 modules · levels 100–200

    estimated price0 of 12 modules published

  • AI Engineering & MLOps · Complete · Levels 100–400

    20 modules · levels 100–400

    estimated price0 of 20 modules published

The five tracks cost the same despite differing module counts: the price reflects the competence a track delivers and the integrative project that closes it, not how many modules it lists. The MLOps track has fewer modules because it reuses less, not because it delivers less.

100Beginner24h

Modules of this track

  • MLOPS-101Syllabus published

    How AI becomes a product (systems view)

    Detailed syllabus not written yet

Reused modules

Comes from elsewhere in the curriculum

  • CORE-101What AI, ML and DL are
  • CORE-102Python for AI
  • CORE-104Data: collection and cleaning
  • CORE-106Your first ML model
  • CORE-108Git, GitHub and your portfolio

200Intermediate36h

Modules of this track

  • MLOPS-201Syllabus published

    Data pipelines

    Detailed syllabus not written yet

  • MLOPS-202Syllabus published

    CI/CD for ML

    Detailed syllabus not written yet

  • MLOPS-203Syllabus published

    Cloud for AI (GPU, storage, cost)

    Detailed syllabus not written yet

Reused modules

Comes from elsewhere in the curriculum

  • CORE-204Evaluation and experimentation
  • CORE-205AI APIs and prompt engineering
  • CORE-206Basic deployment

300Advanced32h

Modules of this track

  • MLOPS-301Syllabus published

    Observability and monitoring

    Detailed syllabus not written yet

  • MLOPS-302Syllabus published

    Serving at scale (batching, caching, autoscaling)

    Detailed syllabus not written yet

Reused modules

Comes from elsewhere in the curriculum

  • CORE-303Training optimisation
  • CORE-304Evaluating AI systems

400Expert40h

Modules of this track

  • MLOPS-401Syllabus published

    AI platform architecture

    Detailed syllabus not written yet

  • EX-MLOPSSyllabus published

    Project — a complete platform

    Content
    Pipeline → training → deployment → monitoring, fully automated

Reused modules

Comes from elsewhere in the curriculum

  • AG-401Agents in production: cost, latency, observability
  • NLP-401Efficient LLMs (quantisation, inference serving)