Build business-critical machine learning models at scale
Azure Machine Learning empowers data scientists and developers to build, deploy, and manage high-quality models faster and with confidence. It accelerates time to value with industry-leading machine learning operations (MLOps), open-source interoperability, and integrated tools. This trusted platform is designed for responsible AI applications in machine learning.
Accelerate time to value
Rapid, customized model development using familiar frameworks supported by flexible, powerful AI infrastructure.
Collaborate and streamline MLOps
Quick ML model deployment, management, and sharing for cross-workspace collaboration and MLOps.
Develop with confidence
Built-in governance, security, and compliance for running machine learning workloads anywhere.
Support for the end-to-end machine learning lifecycle
Label training data and manage labeling projects.
Use with analytics engines for data exploration and preparation.
Access data and create and share datasets.
Use collaborative Jupyter notebooks with attached compute.
Automated machine learning
Automatically train and tune accurate AI models.
Design with a drag-and-drop development interface.
Run experiments and create and share custom dashboards.
CLI and Python SDK
Accelerate the model training process while scaling up and out on Azure compute.
Visual Studio Code and GitHub
Use familiar machine learning tools and switch easily from local to cloud training.
Develop in a managed and secure environment with dynamically scalable CPUs, GPUs, and supercomputing clusters.
Open-source libraries and frameworks
Get built-in support for Scikit-learn, PyTorch, TensorFlow, Keras, Ray RLLib, and more.
Deploy models for batch and real-time inference quickly and easily.
Pipelines and CI/CD
Automate machine learning workflows.
Access container images with frameworks and libraries for inference.
Share and track machine learning models and data.
Hybrid and multicloud
Train and deploy models on premises and across multicloud environments.
Accelerate training and inference and lower costs with ONNX Runtime.
Share and discover models and pipelines across teams in your organization.
Monitoring and analysis
Track, log, and analyze data, models, and resources.
Detect drift and maintain model accuracy.
Debug models and optimize AI model accuracy.
Trace machine learning artifacts for compliance.
Use built-in and custom policies for compliance management.
Enjoy continuous monitoring with Azure Security Center.
Apply quota management and automatic shutdown.
Azure Machine Learning for Deep Learning
Managed end-to-end platform
Streamline the entire deep-learning lifecycle and model management with native MLOps capabilities. Securely run machine learning anywhere with enterprise-grade security. Mitigate model biases and evaluate models with the Responsible AI dashboard.
Any development tools and frameworks
Build deep-learning models with your preferred integrated development environments (IDEs) from Visual Studio Code to Jupyter Notebooks, in the framework of your choice using PyTorch and TensorFlow. Azure Machine Learning interoperates with ONNX Runtime and DeepSpeed to optimize training and inference.
Use purpose-built AI infrastructure designed to combine the latest NVIDIA GPUs and InfiniBand networking solutions up to 400 Gbps. Scale up to thousands of GPUs within a single cluster with unprecedented scale.
Accelerate time to value with rapid model development
Improve productivity with a unified studio experience that supports machine learning tasks. Build, train, and deploy models with Jupyter Notebooks using built-in support for popular open-source frameworks and libraries. Create accurate models quickly with automated machine learning for tabular, text, and image models. Use Visual Studio Code to go from local to cloud training seamlessly, and autoscale with Azure AI infrastructure, powered by the NVIDIA Quantum-2 InfiniBand platform.
Collaborate and streamline model management with MLOps
Streamline the deployment and management of thousands of models in multiple environments using MLOps. Deploy and score models faster with fully managed endpoints for batch and real-time predictions. Use repeatable pipelines to automate workflows for continuous integration and continuous delivery (CI/CD). Share and discover machine learning artifacts across multiple teams for cross-workspace collaboration using registries and managed feature store. Continuously monitor model performance metrics, detect data drift, and trigger retraining to improve model performance.
Build enterprise-grade solutions on a hybrid platform
Put security first across the machine learning lifecycle using the built-in data governance in Microsoft Purview. Take advantage of the comprehensive security capabilities spanning identity, data, networking, monitoring, and compliance, all tested and validated by Microsoft. Secure solutions using custom role-based access control, virtual networks, data encryption, private endpoints, and private IP addresses. Train and deploy models anywhere, from on premises to multicloud, to meet data sovereignty requirements. Govern with confidence using built-in policies and compliance with 60 certifications, including FedRAMP High and HIPAA.
Use responsible AI practices throughout the lifecycle
Evaluate machine learning models with reproducible and automated workflows to assess model fairness, explainability, error analysis, causal analysis, model performance, and exploratory data analysis. Make real-life interventions with causal analysis in the Responsible AI dashboard and generate a scorecard at deployment time. Contextualize responsible AI metrics for both technical and non-technical audiences to involve stakeholders and streamline compliance review.
Build your machine learning skills with Azure
Learn more about machine learning on Azure and participate in hands-on tutorials with a 30-day learning journey. By the end, you'll be prepared to take the Azure Data Scientist Associate Certification.
Key service capabilities for the full machine learning lifecycle
Increase agility in shipping your models by making features discoverable and reusable across multiple workspaces.
Automated machine learning
Rapidly create accurate models for classification, regression, time-series forecasting, natural language processing tasks, and computer vision tasks with automated machine learning.
Comprehensive security and compliance, built in
Microsoft invests more than USD1 billion annually on cybersecurity research and development.
We employ more than 3,500 security experts who are dedicated to data security and privacy.
Pay only for what you need, with no upfront cost
Get started with an Azure free account
After your credit, move to pay as you go to keep building with the same free services. Pay only if you use more than your free monthly amounts.
Learn how customers are using Azure Machine Learning to innovate with AI
Azure Machine Learning resources
Learn how enterprise organizations across industries are using MLOps to overcome the challenges of implementing AI and machine learning technologies.
Learn expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using TensorFlow, Spark, and Kubernetes.
Discover a systematic approach to building, deploying, and monitoring machine learning solutions with MLOps. Rapidly build, test, and manage production-ready machine learning lifecycles at scale.
The Forrester Consulting Total Economic ImpactTM study, commissioned by Microsoft, examines the potential return on investment enterprises may realize with Azure Machine Learning.
Learn how to build more secure, scalable, and equitable machine learning solutions.
Read about tools and methods to better understand, protect, and control your models.
Frequently asked questions about Azure Machine Learning
Azure Machine Learning studio is the top-level resource for Machine Learning. This capability provides a centralized place for data scientists and developers to work with all the artifacts for building, training, and deploying machine learning models.