What libraries are necessary for this skill to function?

You will need the databricks-sdk, mlflow, and scikit-learn libraries installed in your environment.

How does this skill handle model deployment?

It uses Mosaic AI Model Serving to create and manage REST API endpoints with auto-scaling capabilities for real-time inference.

What is the primary purpose of the Databricks Core Workflow B skill?

It is designed to automate and standardize the secondary Databricks workflow, specifically focusing on MLflow model training, feature engineering, and production deployment.

Does this skill require Unity Catalog?

Yes, Unity Catalog is required for utilizing the model registry and the Feature Engineering Client for discoverable features.

Can I use this for batch processing?

Absolutely. The skill includes standardized patterns for batch inference jobs that score data from Delta tables and write results back to production catalogs.

Databricks MLflow Workflow

Name: Databricks MLflow Workflow
Author: jeremylongshore

byjeremylongshore

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1,887

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Data Science & ML

Streamlines the end-to-end machine learning lifecycle on Databricks including feature engineering, MLflow tracking, and model serving.

This skill provides a standardized framework for managing the complete ML lifecycle within the Databricks environment. It enables developers to leverage the Unity Catalog for feature engineering, track experiments with MLflow, manage models using the registry's champion/challenger aliases, and deploy real-time inference endpoints via Mosaic AI. It is an essential tool for data scientists and ML engineers looking to automate production-grade pipelines from data preparation and model training to deployment and batch scoring.

Key Features

01Feature Engineering Client integration for discoverable Unity Catalog features

02Unity Catalog model registry management using champion/challenger aliases

03Comprehensive MLflow experiment tracking with automated logging and metrics

04Standardized batch inference job patterns for daily production scoring

051,887 GitHub stars

06Mosaic AI Model Serving deployment for real-time REST API inference

Use Cases

01Building and deploying automated churn prediction or classification models

02Implementing scalable real-time model serving with auto-scaling endpoints

03Managing complex ML experiment iterations with standardized tracking and signatures

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills databricks-core-workflow-b

For use in Claude.ai and ChatGPT

Key Features

01Feature Engineering Client integration for discoverable Unity Catalog features

02Unity Catalog model registry management using champion/challenger aliases

03Comprehensive MLflow experiment tracking with automated logging and metrics

04Standardized batch inference job patterns for daily production scoring

051,887 GitHub stars

06Mosaic AI Model Serving deployment for real-time REST API inference

Use Cases

01Building and deploying automated churn prediction or classification models

02Implementing scalable real-time model serving with auto-scaling endpoints

03Managing complex ML experiment iterations with standardized tracking and signatures

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills databricks-core-workflow-b

For use in Claude.ai and ChatGPT