Databricks Core Workflow B: MLflow Training

Overview

Build ML pipelines with MLflow experiment tracking, model registry, and deployment.

Prerequisites

• Completed databricks-install-auth setup

• 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

• MLflow on Databricks

• Feature Engineering

• Model Serving

• Unity Catalog ML

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.