• Coding
• Tutorials
• AUTHOR: codevision AI engine
• DZone AI/ML

This tutorial demonstrates how to build a complete machine learning pipeline on Databricks using Delta Lake for data management and MLflow for model tracking. Author harshraj bhoite explains how the Bronze–Silver–Gold framework enables reproducible, scalable workflows from raw data ingestion to production deployment. The guide covers data cleansing, feature engineering, model training, and batch scoring with practical code examples. It is worth reading because it provides a structured approach to operationalizing ML pipelines in enterprise environments. Readers will learn to implement end-to-end workflows using Databricks’ integrated tools for data engineering and model governance.

Key facts

• Databricks combines Delta Lake, Auto Loader, and MLflow to streamline ML pipelines from data ingestion to deployment
• The Bronze–Silver–Gold architecture ensures data quality through staged transformations and feature engineering
• MLflow tracks model parameters, metrics, and artifacts while enabling version control and deployment
• Automated promotion workflows and Delta Live Tables ensure scalable, governed ML operations
• The tutorial includes code samples for data cleansing, model training, and batch inference implementation