How to Build an End-to-End Production-Grade Machine Learning Pipeline with ZenML, Including Custom Materializers, Metadata Tracking, and Hyperparameter Optimization

A ZenML tutorial shows how to build a production-style ML pipeline with a custom materializer and metadata logging.

It covers loading and preprocessing the breast cancer dataset, then running a fan-out search across multiple models.

Models like RandomForest, GradientBoosting, and LogisticRegression are evaluated with hyperparameter optimization and artifact tracking.

A fan-in step selects the best model and promotes it, highlighting a reproducible and observable production workflow.