AI

AI DevOps Engineer

Structured training in the automation and infrastructure practices specific to shipping AI systems — containerizing models, CI/CD for ML, and continuous monitoring in production.

50
hours of live training
10 weeks
at one hour a day, weekdays
4
modules
24
topics covered

Before you start

Some technical background helpful. If you are not sure whether that describes you, the consultation exists to answer exactly that — and to tell you if a different programme is the better start.

Price

United Kingdom
£750
Canada
$1,750

Tax included — the price shown is the price you pay. This covers the training stage. The bootcamp and placement stages are priced separately.

Curriculum

A module-by-module breakdown

Everything covered in the program, in the order you'll cover it.

01DevOps Foundations

12 hours

The delivery engineering underneath MLOps

  • Git branching & trunk-based development
  • CI/CD with GitHub Actions & GitLab
  • Docker images & registries
  • Kubernetes workloads & Helm
  • Terraform infrastructure as code
  • Secrets management & least privilege

02ML Pipelines & Automation

15 hours

Automate the model lifecycle

  • Training pipeline orchestration
  • Kubeflow Pipelines & Airflow
  • Data & model versioning with DVC
  • MLflow registry & stage promotion
  • Automated retraining triggers
  • Reproducible environments

03Serving & Scaling

8 hours

Run inference reliably

  • Model servers: Triton, KServe, vLLM
  • GPU scheduling & node pools
  • Autoscaling & request batching
  • Canary & blue-green model releases
  • Cost optimisation for GPU workloads
  • Load testing inference endpoints

04Observability & Governance

15 hours

Know when a model breaks

  • Prometheus & Grafana for ML metrics
  • Drift & data quality monitoring
  • Distributed tracing for AI services
  • Alerting & incident runbooks
  • Audit trails & model lineage
  • A capstone MLOps platform

Projects

Five projects, in AI DevOps Engineer

The bootcamp runs the same five stages whatever you enrolled in, so the portfolio you finish with is deep in one technology rather than shallow across several. Each is scoped, designed, built and reviewed.

  1. 01 · Foundations

    A first build that puts the core concepts to work — environments configured, the basic workflows exercised, the fundamentals proven rather than assumed.

  2. 02 · Multiple moving parts

    A larger solution with several components and an integration or two, applying the practices that are conventional in your stack rather than improvising.

  3. 03 · Architecture that scales

    A design-led build: scalability, monitoring and the production-grade patterns your field actually uses, decided before they are implemented.

  4. 04 · An enterprise scenario

    A realistic situation with services that depend on each other, security to harden and performance to tune — the kind of work that is waiting on the other side of the offer.

  5. 05 · Capstone

    End to end, everything together: planned, designed, built, deployed and documented well enough to sit at the top of your GitHub profile.

What's included

Everything in the programme

The same seven things in every programme on the site — the subject is what changes.

  • Live training

    Taught live by a practitioner, not pre-recorded video you work through alone.

  • 50 hours

    Per programme as standard — 60 for IT Support Analyst, 70 for the AI Consultant and cloud engineering tracks.

  • Projects

    Five briefs in your own stack, each scoped, designed, built and reviewed the way a delivery team would.

  • Mentoring

    One mentor who stays with you for the whole programme, rather than whoever is free that week.

  • Resume & LinkedIn

    Both rewritten around the work you actually built, and positioned for the roles you are targeting.

  • Interview prep

    Mock interviews on the technical and the competency side, including defending the projects you built.

  • Placement support

    Your profile put in front of employers, and the search continues until you are hired.

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