modelsIntermediate

Machine Learning

Track experiments with MLflow, build features, register models in Unity Catalog and serve them behind an endpoint.

0/17|17 written|~50 h

To doIn progressDonePlannedClick a concept to open its cardSwipe the map, tap a concept

Stage 1 · Foundations

Architecture of the Data Intelligence Platform Columns, rows, and DataFrame structure

Stage 2 · Experiment

MLflow tracking on Databricks Feature engineering and the feature store MLflow Tracing for GenAI applications AutoML Training and tuning a classic model MLflow 3 for models AI Runtime and serverless GPU compute Training sets and point-in-time joins Feature Views

Stage 3 · Register and serve

Models in Unity Catalog Model serving endpoints Serving compute and scaling Online Feature Store MLflow deployment jobs Monitoring a deployed model

Everything here assumes the data is already governed and clean, so do Lakehouse Foundations first if that is not true yet.

The order matters: tracking gives you something to compare, features give you something reproducible, the registry gives you something to promote, and serving gives you something to call. Skipping to serving is how models end up unversioned in a notebook.

Resources for this path

Courses, books and repos that cover the whole map go here. None have been added yet; per-concept resources appear on each concept page.