How to Build a Production MLOps Pipeline on Azure Databricks with Spark and MLflow

A technical tutorial published on DEV Community outlines how to construct a production-grade feature engineering pipeline using Azure Databricks for large-scale machine learning workloads. The guide leverages Apache Spark for distributed data transformation, Delta Lake for versioned and ACID-compliant feature storage, and MLflow for tracking pipeline runs and model experiments. The architecture follows the Medallion pattern, organizing data across Bronze, Silver, and Gold layers that progressively clean and enrich raw data before model training. A customer churn prediction system serves as the primary use case, though the author notes the patterns are broadly applicable to any ML feature pipeline. Code examples demonstrate append-only Bronze ingestion, Silver-layer deduplication and schema enforcement, and Gold-layer feature aggregation using PySpark and Delta Lake merge operations.