Data science & ML platforms · Microsoft

Azure Machine Learning

Microsoft's cloud platform for building, training, and operationalizing machine learning and generative AI models.

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Azure Machine Learning is Microsoft's managed ML platform, offering a drag-and-drop designer, automated ML, notebook-based development, and MLOps tooling for deploying and monitoring models in production. Prompt flow extends the platform to building and evaluating applications on top of large language models. It integrates with Azure DevOps and GitHub Actions for CI/CD, and with Azure Synapse Analytics and Azure Data Lake for data access. There is no separate license fee for the Azure Machine Learning service itself; costs come from the underlying compute, storage, and networking resources consumed, billed through the standard Azure pay-as-you-go, savings plan, or reserved-instance options. It suits organizations already standardized on the Azure ecosystem.

At a glance

Vendor Microsoft
Pricing model Usage-based
Free tier Yes
Deployment Cloud
Open source No
Best for Organizations standardized on Azure wanting integrated MLOps and generative AI tooling.

Pricing

No charge for the Azure Machine Learning service itself; billed only for underlying compute, storage, and networking consumed.

Checked on the vendor's own page on September 21, 2026: no prices are published. Expect to be quoted.

Features

  • Drag-and-drop designer for pipeline building
  • Automated ML for tabular, vision, and NLP tasks
  • Prompt flow for LLM application development
  • Managed online and batch endpoints
  • Responsible AI dashboard
  • Distributed training support
  • Model registry and versioning

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Amazon SageMaker AWS's fully managed service for building, training, and deploying machine learning models at cloud scale. Google Vertex AI Google Cloud's unified platform for AutoML, custom model training, and access to foundation models. Dataiku Collaborative platform spanning data preparation and machine learning, used by both analysts and data scientists.

Azure Machine Learning in the index now

Terms to know

Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Predictive analytics Analytics that uses historical data and statistical models to estimate what is likely to happen next. Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. Data lake A storage repository that holds raw data of any format cheaply, with structure applied only when the data is read. MLOps (machine learning operations) The set of practices for deploying, monitoring and maintaining machine learning models reliably in production, extending DevOps to ML systems. Responsible AI A set of practices for developing and deploying AI systems that are fair, transparent, safe, and accountable to the people they affect.

Related guides

How to choose a data science & ML platform The decisions that matter are who builds the models, how automated the modeling should be, and whether you want a cloud-native platform or a portable one.