Module 10 - AI Platform Engineering
"A great ML platform is invisible. Data scientists just know their models ship, monitor, and improve - they never think about the infrastructure underneath."
The difference between a Level-0 MLOps organization (where every model deployment is a heroic manual effort) and a Level-3 organization (where models are continuously trained, tested, and deployed automatically) is entirely an infrastructure problem. This module teaches you to build that infrastructure.
What You'll Learn
By the end of this module you will be able to design and implement every major component of an internal ML platform - from experiment tracking and model registry to feature platforms and Kubernetes-native ML workloads. You will understand the MLOps maturity model, know which components to build vs buy, and understand how to design platforms that data scientists actually want to use.
Module Map
Lessons in This Module
| # | Lesson | Core Skill |
|---|---|---|
| 01 | MLOps Platform Architecture | MLOps maturity model and roadmap |
| 02 | Experiment Tracking | Govern 50 scientists on one MLflow instance |
| 03 | Model Registry & Versioning | 3-minute rollback via model registry |
| 04 | CI/CD for ML | Automated quality gates for model deployment |
| 05 | Feature Platform | Shared feature infrastructure across teams |
| 06 | Model Monitoring Platform | Catch silent model degradation in 24 hours |
| 07 | Kubernetes for ML | GPU scheduling and ML workloads on K8s |
| 08 | Self-Service ML Platform | Build platforms that data scientists love |
Key Concepts
- MLOps maturity levels - the four-level model from ad-hoc to fully automated
- Model lineage - connecting model version to data version to code version
- Feature store - the shared infrastructure that eliminates feature duplication
- Data drift vs concept drift - two distinct failure modes requiring different responses
- Platform developer experience - why adoption, not features, determines platform success
Why This Module Matters
The bottleneck in most ML organizations is not model quality - it is the infrastructure required to take a trained model from a Jupyter notebook into reliable production operation at scale. Platform engineering is the discipline that removes that bottleneck. It is the difference between an ML team that ships one model per quarter and one that ships one per week.