Within data-centric organizations, creating an end-to-end machine learning (ML) Pipeline that is reproducible, scalable, and traceable is an essential component. The integrated ecosystem of Delta Lake, Auto Loader, and MLflow in Databricks allows organizations to simplify the ML lifecycle from unrefined data ingestion all the way to production deployment.
This tutorial provides a comprehensive guide on constructing an end-to-end ML pipeline on Databricks, utilizing MLflow for model tracking and the model registry, and leveraging Delta Lake for data management. We will demonstrate all the tasks in a unified workflow, including raw data ingestion, feature preparation, model training, and prediction serving.