Skill UI

This guide outlines the complete MLOps workflow on Databricks. It covers feature engineering using Feature Store, tracking experiments with MLflow, managing model versions in the Model Registry, and deploying real-time inference endpoints via Mosaic AI Model Serving. Ideal for building robust, production-grade ML pipelines.

Design and implement complete machine learning pipelines using multi-agent orchestration following modern MLOps best practices. Covers data engineering, feature stores, model training, and production deployment. Ideal for building scalable, monitored, and reproducible ML systems in production environments.