How do I install the Databricks Core Workflow B skill?

Run npx skillmds@latest add micsapp/databricks-core-workflow-b in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Databricks Core Workflow B free to use?

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Who published Databricks Core Workflow B?

micsapp (@micsapp) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Databricks Core Workflow B by micsapp

Name: Databricks Core Workflow B
Rating: 4.5 (4 reviews)
Author: micsapp

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".

micsapp Updated Sep 8, 2026 3 repo stars

File contents

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

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.

---name: databricks-core-workflow-bdescription: 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".license: MIT---# Databricks Core Workflow B: MLflow Training## OverviewBuild 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 InferenceFor 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 StepsFor 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.

Frequently asked questions