databricks-ml-training

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.