Dataset Management in Azure Machine Learning Pipelines

In machine learning, high-quality model training depends on how effectively you organize, version, and access your data. This course teaches you how to manage data assets within Azure Machine Learning pipelines, ensuring your workflows are secure, reproducible, and production-ready. You will transition from manually handling raw files to building structured, version-controlled datastores and datasets. Through clear explanations and practical text-based exercises, you will master the foundational data operations required for modern machine learning pipelines. What you'll learn: - Understand core dataset concepts, datastores, and storage integration within Azure - Configure secure connections to public and private data sources using the Azure portal and CLI - Create, register, and version datasets to ensure experiment reproducibility - Apply modern MLOps concepts like data lineage and drift monitoring within your pipelines - Integrate registered datasets into training scripts and pipeline steps - Manage access control and data security policies for collaborative environments We begin with the absolute basics of cloud storage and Azure ML workspace terminology before moving into hands-on configuration. You will read through step-by-step guides on registering datastores, versioning datasets, and consuming data within automated pipelines. This course is designed for beginner data scientists, machine learning engineers, and cloud enthusiasts who want to learn structured data management in Azure. No prior experience with Azure Machine Learning is required, though a basic understanding of cloud concepts is helpful. Start reading today to build reliable, data-driven machine learning pipelines in the cloud.