databricks-ml-training

Train, register, and serve ML models on Databricks with MLflow and Unity Catalog.

Updated Jul 4, 2026

Search Tags:#MLflow #Databricks #Unity Catalog #Machine Learning #Model Deployment #Serverless

Ready to run

npx skills add https://github.com/mkgs-databricks-demos/aiSkillUpdater --skill databricks-ml-training

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: databricks-ml-training
Source: https://github.com/mkgs-databricks-demos/aiSkillUpdater/tree/main/databricks-ml-training
Command: npx skills add https://github.com/mkgs-databricks-demos/aiSkillUpdater --skill databricks-ml-training

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires mlflow, databricks-sdk, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps users train and deploy machine learning models on Databricks using MLflow for tracking and Unity Catalog for registration and model serving.

Core Features & Use Cases

MLflow Integration: Tracks and logs model training runs.
Unity Catalog Integration: Registers models to Unity Catalog for model serving.
Serverless Deployment: Deploy models as serverless jobs.
Batch Scoring: Score data using registered models via Spark UDF.
Real-time Serving: Serve models in real-time using endpoints.
Custom PyFunc Models: Log and serve custom Python-based ML models.
Custom GenAI Agents: Log and serve custom GenAI agents using MLflow ResponsesAgent.
Use Case: Users can train a model for predicting customer churn and serve it in real-time using an endpoint to score incoming data.

Quick Start

Train a classification model on Databricks and deploy it as a serverless job for real-time scoring.

Frequently Asked Questions about databricks-ml-training

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I deploy machine learning models on Databricks using MLflow?▼

To deploy machine learning models on Databricks, use MLflow to track training runs and register models to Unity Catalog. This enables serverless deployment, batch scoring via Spark UDF, and real-time serving endpoints.

Can I serve custom Python models in real-time using Databricks Serverless?▼

Yes, you can log and serve custom PyFunc models and GenAI agents using MLflow. Once registered to Unity Catalog, these models can be deployed as serverless jobs for real-time scoring on Databricks.

Do I need Unity Catalog access to serve ML models on Databricks?▼

Yes, Unity Catalog access is required. It acts as the central governance layer to register trained MLflow models before deploying them to serverless endpoints or using them for batch scoring via Spark UDF.

What is the best way to score batch data with registered MLflow models in Databricks?▼

The best way to score batch data is by invoking registered MLflow models through Spark UDFs. This allows you to apply machine learning models to large datasets directly within Databricks.

How does MLflow track model training runs on Databricks?▼

MLflow tracks model training runs by logging parameters, metrics, and artifacts during the training process. This Databricks integration ensures all machine learning experiments are recorded before Unity Catalog registration.