Databricks Core Workflow B: MLflow Training
Overview
Build ML pipelines with MLflow experiment tracking, model registry, and deployment.
Prerequisites
- Completed
databricks-install-authsetup - Familiarity with
databricks-core-workflow-a(data pipelines) - MLflow and scikit-learn installed
- Unity Catalog for model registry (recommended)
Instructions
Step 1: Feature Engineering with Feature Store
Step 2: MLflow Experiment Tracking
Step 3: Model Registry and Versioning
Step 4: Model Serving and Inference
For full implementation details and code examples, load: references/implementation-guide.md
Output
- Feature table in Unity Catalog
- MLflow experiment with tracked runs
- Registered model with versions
- Model serving endpoint
Error Handling
| Error | Cause | Solution |
|---|---|---|
Model not found | Wrong model name/version | Verify in Model Registry |
Feature mismatch | Schema changed | Retrain with updated features |
Endpoint timeout | Cold start | Disable scale-to-zero for latency |
Memory error | Large batch | Reduce batch size or increase cluster |
Resources
Next Steps
For common errors, see databricks-common-errors.
Examples
Basic usage: Apply databricks core workflow b to a standard project setup with default configuration options.
Advanced scenario: Customize databricks core workflow b for production environments with multiple constraints and team-specific requirements.