# Databricks Core Workflow B

> Execute Databricks secondary workflow: MLflow model training and deployment. Use when building ML pipelines, training models, or deploying to production. Trigger with phrases like "databricks ML", "mlflow training", "databricks model", "feature store", "model registry".

- Skill: `micsapp/databricks-core-workflow-b` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add micsapp/databricks-core-workflow-b`
- Raw SKILL.md: https://api.skillmd.com/api/skills/micsapp/databricks-core-workflow-b/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: micsapp (https://skillmd.com/u/micsapp)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/micsapp/databricks-core-workflow-b

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# 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](https://docs.databricks.com/mlflow/index.html)
- [Feature Engineering](https://docs.databricks.com/machine-learning/feature-store/index.html)
- [Model Serving](https://docs.databricks.com/machine-learning/model-serving/index.html)
- [Unity Catalog ML](https://docs.databricks.com/machine-learning/manage-model-lifecycle/index.html)

## 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.