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Machine learning for data scientists

Explore machine learning tools for data scientists and machine learning engineers and learn how to build cloud-scale machine learning solutions on Azure.

Discover machine learning on Azure

Build and deploy machine learning models for mission-critical processes responsibly and on your terms with Azure tools and services.

Develop machine learning models on your terms

Build machine learning models in your preferred development language, environment, and machine learning frameworks using the tools of your choice and deploy your models to the cloud, on-premises, or at the edge with Azure AI.

Build machine learning solutions responsibly

Understand your machine learning models, protect data with differential privacy and confidential computing, and control the machine learning lifecycle with audit trials and datasheets.

Confidently deploy machine learning models for business-critical processes

Deploy and manage highly scalable, fault tolerant, and reproducible machine learning solutions.

See how other data scientists are using Azure Machine Learning

See how organizations are using Azure to support their mission-critical workloads.


See how Humana delivers AI-enabled mission-critical healthcare experiences.


Learn how AGL implemented MLOps with Azure Machine Learning.


Discover how UCLA is pioneering the use of AI to assist its doctors.

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Explore machine learning through videos

Explore how to use machine learning solutions to support mission-critical applications.

Securing your machine learning environments

See how to use Azure to access enterprise-grade security and governance.

Hybrid and multi-cloud machine learning

See how to provision hybrid and multi-cloud machine learning environments.

Open and interoperable machine learning

See how Azure Machine Learning works with open-source technologies and integrates with other Azure services.

Training machine learning models at scale

Understand how to utilize the right compute on Azure to scale your training jobs.

Model deployment and inferencing

Learn about the various deployment options and optimizations for large-scale model inferencing.

MLOps explained

Learn about the importance of MLOps and the processes associated with it.

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MLOps with Azure Machine Learning

Accelerate the process of building, training, and deploying machine learning models at scale.

Machine Learning solutions with enterprise security and scale

Learn how to build secure, scalable, and equitable machine learning solutions with Azure Machine Learning.

Responsible AI with Azure Machine Learning

Explore tools and methods to help you understand, protect, and control your machine learning models.

Learn more through example solution architectures

Explore different scenarios for using Azure Machine Learning.

Machine learning

Control the model training process with adjustable parameters called hyperparameters. Explore recommended practices for tuning the hyperparameters of Python models and see how to automate hyperparameter tuning and run experiments in parallel to efficiently optimize hyperparameters.

Deep learning

See how to conduct distributed training of deep machine learning models across clusters of GPU-enabled virtual machines. This scenario is for image classification, but the solution can be generalized to other deep learning scenarios such as segmentation or object detection.


Learn how to implement continuous integration (CI), continuous delivery (CD), and retraining pipeline for an AI application using Azure DevOps and Azure Machine Learning. The solution is built on the scikit-learn diabetes dataset but can be easily adapted for any AI scenario and other popular build systems.

Edge deployment

See how to use Azure Stack Edge to extend rapid machine learning inference from the cloud to on-premises or edge scenarios. Use Azure Stack Edge to take advantage of Azure capabilities like compute, storage, networking, and hardware-accelerated machine learning for any edge location.

Batch scoring

Learn how to use Azure Machine Learning to apply neural style transfer, a deep learning technique that composes an existing image in the style of another image, to a video.

Real-time scoring

Explore how to deploy Python models as web services to make real-time predictions using Azure Kubernetes Service (AKS). Machine learning models deployed on AKS are suitable for high-scale production deployments.