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.
See how organizations are building innovative machine learning solutions on their own terms with Azure.
See how Avanade uses Azure Machine Learning to improve employee experiences.
Explore machine learning through videos
Explore how to use machine learning solutions to support mission-critical applications.
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.
Learn about the importance of MLOps and the processes associated with it.
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.
See how to use Azure machine learning products and services to build machine learning models on your terms.
Fundamentals of machine learning in the cloud
Get an intro to machine learning and explore the key phases of the machine learning lifecycle.
Machine learning tools in Azure
Explore machine learning tools for data scientists and see how they work on Azure.
Deep learning fundamentals with PyTorch
See how to use PyTorch to solve a simple image classification problem.
Run machine learning anywhere
Run machine learning on-premises or in multi-cloud using existing Kubernetes infrastructure.
Learn the basics of PyTorch
Watch a tutorial with PyTorch Developer Advocate Suraj Subramanian.
Build responsible AI using Error Analysis toolkit
See how to identify model errors and diagnose the root causes.
Tagging audio using deep learning
Learn how to use sounds, convert them into images and build a classifier model to tag songs according to mood.
Reproducible data science with machine learning
Learn how to organize a reproducible workflow.
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.
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.
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.
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.
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.
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.
AI updates, blogs, and announcements
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