The infrastructure that makes models trustworthy.
Models are the easy part. This track is the pipelines, feature stores, and operational habits that turn raw data into systems you can rely on in production.
Who it's for
Built for these people.
Software & data engineers supporting ML teams.
Analysts moving toward engineering.
Teams whose models work in notebooks but not in production.
Backend developers who now own data infrastructure.
What you'll be able to do
By the end.
Design data models and warehouses built for ML.
Build orchestrated, observable pipelines.
Stand up a feature store.
Monitor models and data once they're live.
The Curriculum
From data model to a pipeline feeding a live system.
A structured sequence — each module builds on the last, with assessments along the way.
Data modelling & warehousing
Schemas, storage, and the foundations everything downstream depends on.
Pipelines & orchestration
Reliable batch and streaming pipelines that don't silently fail.
Feature engineering & stores
Turning raw data into features — served consistently to training and production.
MLOps & deployment
Packaging, versioning, and shipping models the way real teams do.
Monitoring & reliability
Data drift, model decay, and the observability that catches problems before users do.
Capstone
Build a production-grade pipeline that feeds and monitors a live model.
How you're certified
Earned, not awarded for attendance.
Enrol
Join a cohort or start self-paced, placed at the right level for your background.
Learn
Work through the modules with short assessments at each stage.
Build
Complete the capstone — real work, reviewed against a published rubric.
Certify
Earn a GIAI Certificate tied to what you actually produced.
Admissions
Request your place.
We'll send the full syllabus for this track and the next intake dates.