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Specialist track

MLOps Engineering

The track for turning machine-learning models into dependable products. You learn to build data and training pipelines, deploy models as services, and monitor them in production. The operational discipline that keeps ML systems working after the demo. Python throughout, project-driven, taught by practitioners.

Duration
12 weeks
Commitment
~12 hrs/week · part-time, evenings
Format
Live online
Level
Intermediate · comfortable with Python

Specialist track

$350

Indicative · your local currency · pre-VAT-determination

Deposit $90, then monthly installments

Cohort 1 · Online

Starts 12 October 2026

Enrolling
Apply now

A short application. No payment on this site.

Refund & deferral policy

What you'll learn

  • Reproducible pipelines: data versioning, training and experiment tracking
  • Packaging and serving models as reliable APIs with Docker
  • Deploying and scaling model services on Kubernetes in the cloud
  • Monitoring models in production: drift, performance and data quality
  • Automating retraining and rollout with CI/CD for ML
  • Working AI-natively: building pipelines and services with AI coding agents. Spec, review, verify
AI-native by default

The AI classes in this program

Every Lightspace program teaches the AI-native method. Not a bolt-on module, but a working style woven through the projects.

  • AI-assisted pipeline and service development. Spec, review, verify on every build
  • Evals as an operational habit: measuring model and AI output quality systematically
  • Context engineering for ML codebases. Reproducible conventions agents can follow
  • AI-assisted debugging of data, training and serving issues

The curriculum

  1. 1

    Pipelines & tracking

    Weeks 1, 3

    Reproducible data and training pipelines, versioning, and experiment tracking with MLflow.

  2. 2

    Packaging & serving

    Weeks 4, 6

    Turning models into containerised services with FastAPI and Docker, tested and versioned.

  3. 3

    Deploy & scale

    Weeks 7, 9

    Deploying model services to Kubernetes in the cloud, orchestrated with Airflow.

  4. 4

    Monitor & automate

    Weeks 10, 12

    Drift and performance monitoring, automated retraining, and a capstone deployment you present.

What you leave with

  • A deployed, monitored model service with a reproducible pipeline
  • An experiment-tracking and deployment workflow you can take to work
  • The judgement to keep ML systems reliable in production

How you're taught

Live cohorts led by working practitioners. Not pre-recorded videos.

Live, not recorded

Real classes on a schedule, with a group that keeps you moving.

Practitioner instructors

Taught by people who build and ship software for a living.

Feedback on your work

Code review and honest feedback on real projects, every week.

Questions

Where does it take place?

Live online. You can join from anywhere in the world.

How much time does it take?

12 weeks, at roughly ~12 hrs/week · part-time, evenings.

What do I need to start?

Intermediate · comfortable with Python.

How do payments work?

There's no payment on this website. You submit a short application; if you're accepted, our team emails you to confirm your place and arrange payment. A deposit, then monthly installments.

What language is it taught in?

All live classes and materials are in English.

How much Python do I need?

You should be comfortable writing Python and have seen a machine-learning model trained before. You don't need to be a data scientist. The focus is the engineering around models.

Full details in our Refund, withdrawal & deferral policy.