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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 — with optional in-person lab days in Nairobi
Level
Intermediate · comfortable with Python

Specialist track

$1,450

Indicative · your local currency · pre-VAT-determination

Deposit $350, 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. In-person lab days at our Nairobi base are optional, not required.

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.