Azure Machine Learning Studio

Azure Machine Learning (Azure ML) is a managed cloud platform for the full machine learning lifecycle — data preparation, experiment tracking, model training, evaluation, deployment, and monitoring. Unlike Azure AI Services (which provides pre-built AI), Azure ML lets you train your own models on your own data using any ML framework: scikit-learn, TensorFlow, PyTorch, XGBoost, and more. It is the preferred platform for data scientists and ML engineers working in Azure.

An Azure ML workspace is the top-level resource that ties together all ML resources: compute clusters, datasets, experiments, models, and deployments. When you create a workspace, Azure automatically provisions a linked Storage account (for data), Key Vault (for secrets), Application Insights (for deployment monitoring), and optionally a Container Registry (for custom environments). Everything in an ML project lives inside a single workspace, making collaboration and governance straightforward.