A Practical Guide to Building End-to-End Azure Machine Learning Training Pipelines with Python SDK v2

This paper delivers a practical, step-by-step guide to constructing, automating, and managing machine learning training pipelines using the Azure Machine Learning (Azure ML) Python SDK v2. It systematically navigates the essential stages, commencing with Azure ML workspace connection and the programmatic setup of requisite compute infrastructure. The guide then details the creation of versioned foundational MLOps assets, including Data Assets for traceable data handling, custom Conda-based Environments for execution reproducibility, and modular Components for discrete pipeline tasks such as data preparation and model training. Emphasis is placed on integrating MLflow for comprehensive experiment tracking and model registration. The methodology culminates in the assembly and execution of an end-to-end training pipeline, exemplified by an NYC Taxi fare prediction model, illustrating the orchestration of these elements into a cohesive workflow. This tutorial aims to empower developers and MLOps practitioners with the skills to develop modular, scalable, and reproducible ML solutions in the Azure cloud environment.