Course 3: MLOps Engineering on Databricks
Subtitle
Build and Deploy ML Systems with MLflow, Feature Store, and Model Serving
Description
Master the complete MLOps lifecycle on Databricks: experiment tracking with MLflow, feature engineering with Feature Store, model management with Unity Catalog, and deployment with Model Serving. Understand each component deeply by building equivalent systems from scratch with the Sovereign AI Stack.
Learning Outcomes
- Track experiments and manage model lifecycle with MLflow on Databricks
- Build and serve features using Databricks Feature Store and SQL Warehouses
- Register, version, and govern models with Unity Catalog
- Deploy models for batch and real-time inference
- Implement quality gates and monitoring for production ML
Duration
~30 hours | 38 videos | 12 labs | 5 quizzes | 1 capstone
Weeks
| Week | Topic | Sovereign AI Stack |
|---|---|---|
| 1 | Experiment Tracking with MLflow | reqwest, serde, pacha |
| 2 | Feature Engineering | alimentar, trueno, delta-rs |
| 3 | Model Training and Registry | aprender, pacha |
| 4 | Model Serving and Inference | realizar |
| 5 | Production Quality and Orchestration | pmat, batuta |
| 6 | Capstone: Fraud Detection Platform | Full stack |
Databricks Free Edition Features Used
- Experiments (MLflow Tracking)
- Catalog (Unity Catalog for model registry)
- Jobs & Pipelines (orchestration)
- SQL Warehouses (feature computation)
- Playground (model testing)