ml-pipeline-setup

What problem does it solve?

This Skill streamlines the creation of robust, production-grade Machine Learning pipelines on Databricks, ensuring consistency and reliability from feature engineering to model deployment.

Core Features & Use Cases

• End-to-End ML Workflow: Covers feature table creation, model training, and batch inference.
• Feature Engineering Integration: Leverages Databricks Feature Store for training-serving consistency.
• MLflow & Unity Catalog: Integrates seamlessly with MLflow for tracking and Unity Catalog for model registry.
• Use Case: Automate the entire process of building and deploying a predictive model, from raw data in the Gold layer to generating predictions on new data, ensuring best practices are followed at each step.

Quick Start

Use the ml-pipeline-setup skill to create feature tables for your cost data.