Azure Machine Learning

Azure Machine Learning is a cloud platform for building, training, and deploying machine learning and AI models. Data scientists use it for AutoML, short for automated machine learning, which handles classification and forecasting tasks, and Prompt Flow, for designing workflows around language models. A model catalog offers options from Microsoft, OpenAI, Hugging Face, Meta, and Cohere. Azure Machine Learning Studio is the central workspace where teams manage data, experiments, and deployments. It connects to other Azure services, such as Blob Storage, Key Vault, and Container Registry, for secrets management and model storage. It is made by Microsoft, which states the platform covers more than 100 compliance certifications and runs under a 99.9% uptime service level agreement.

This fits a company already standardized on Microsoft's cloud that wants a managed way to build and track machine learning models rather than buying separate tools. A reasonable starting point is one team piloting AutoML on an existing dataset, since AutoML can produce a working model without heavy coding. There is no charge for the platform itself; costs come from the compute instances used for training and hosting, so run a small workload first to get a realistic monthly estimate. Because pricing depends entirely on compute choice, a team new to cloud cost management can overspend on GPU instances left running, so pair this with someone who can watch usage and set spending alerts from day one.