Azure Machine Learning
Use an enterprise grade service for the end to end machine learning life cycle
Support for the end to end machine learning life cycle
Data labeling
Label training data and manage labeling projects.
Data preparation
Use with analytics engines for data exploration and preparation.
Datasets
Access data and create and share datasets.
Notebooks
Use collaborative Jupyter notebooks with attached compute.
Automated machine learning
Automatically train and tune accurate models.
Drag-and-drop designer
Design with a drag-and-drop development interface.
Experiments
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 tools and switch easily from local to cloud training.
Compute instance
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.
Managed endpoints
Deploy models for batch and real-time inference quickly and easily.
Pipelines and CI/CD
Automate machine learning workflows.
Prebuilt images
Access container images with frameworks and libraries for inference.
Model repository
Share and track models and data.
Hybrid and multicloud
Train and deploy models on premises and across multicloud environments.
Optimize models
Accelerate training and inference and lower costs with ONNX Runtime.
Monitoring and analysis
Track, log, and analyze data, models, and resources.
Data drift
Detect drift and maintain model accuracy.
Error analysis
Debug models and optimize model accuracy.
Auditing
Trace machine learning artifacts for compliance.
Policies
Use built-in and custom policies for compliance management.
Security
Enjoy continuous monitoring with Azure Security Center.
Cost control
Apply quota management and automatic shutdown.
Azure Machine Learning for Deep Learning
Managed end-to-end platform
Streamline the entire deep learning lifecycle and management of models with native MLOps capabilities. Run machine learning anywhere securely 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 favorite IDEs from Visual Studio Code to Jupyter Notebooks and in the framework of your choice with PyTorch and TensorFlow. Azure Machine Learning integrates with ONNX Runtime and DeepSpeed to optimize your training and inference.
World Class Performance
Leverage purpose-built AI infrastructure uniquely designed to combine the latest NVIDIA GPUs and Mellanox Networking up to 200GB/s InfiniBand interconnects. Scale up to thousands of GPUs within a single cluster with unprecedented scale.
Accelerate time to value with rapid model development
Improve productivity with the studio capability, a development experience that supports all machine learning tasks, to build, train, and deploy models. Collaborate 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 using feature engineering and hyperparameter sweeping. Use Visual Studio Code to go from local to cloud training seamlessly, and autoscale with powerful cloud-based CPU and GPU clusters powered by NVIDIA Quantum InfiniBand network.
Operationalize at scale 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. Continuously monitor model performance metrics, detect data drift, and trigger retraining to improve model performance.
Deliver responsible machine learning solutions
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.
Innovate on a hybrid platform that's more secure and compliant
Increase security across the machine learning lifecycle with comprehensive capabilities spanning identity, data, networking, monitoring, and compliance. Secure solutions using custom role-based access control, virtual networks, data encryption, private endpoints, and private IP addresses. Train and deploy models on premises to meet data sovereignty requirements. Govern with built-in policies and streamline compliance with 60 certifications, including FedRAMP High and HIPAA.
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
Data labeling
Create, manage, and monitor labeling projects, and automate iterative tasks with machine learning–assisted labeling.
Data preperation
Quickly iterate on data preparation at scale on Apache Spark clusters within Azure Machine Learning, interoperable with Azure Synapse Analytics.
Collaborative Notebooks
Maximize productivity with IntelliSense, easy compute and kernel switching, and offline notebook editing. Launch your notebook in Visual Studio Code for a rich development experience, including secure debugging and support for Git source control.
Automated machine learning
Rapidly create accurate models for classification, regression, time-series forecasting, natural language processing tasks, and computer vision tasks. Use model interpretability to understand how the model was built.
Drag-and-Drop machine learning
Use machine learning tools like designer for data transformation, model training, and evaluation, or to easily create and publish machine learning pipelines.
Reinforcement learning
Scale reinforcement learning to powerful compute clusters, support multiple-agent scenarios, and access open-source reinforcement-learning algorithms, frameworks, and environments.
Responsible Building
Get model transparency at training and inferencing with interpretability capabilities. Assess model fairness through disparity metrics and mitigate unfairness. Improve model reliability and identify and diagnose model errors with the error analysis toolkit. Help protect data with differential privacy.
Experimentation
Manage and monitor runs or compare multiple runs for training and experimentation. Create custom dashboards and share them with your team.
Registries
Use organization-wide repositories to store and share models, pipelines, components, and datasets across multiple workspaces. Automatically capture lineage and governance data using the audit trail feature.
Git & Github
Use Git integration to track work and GitHub Actions support to implement machine learning workflows.
Managed Endpoints
Use managed endpoints to operationalize model deployment and scoring, log metrics, and perform safe model rollouts.
Autoscaling Compute
Use purpose-built AI supercomputers to distribute deep learning training and to rapidly test, validate, and deploy models. Share CPU and GPU clusters across a workspace and automatically scale to meet your machine learning needs.
Interoperability with other Azure Services
Accelerate productivity with Microsoft Power BI and services such as Azure Synapse Analytics, Azure Cognitive Search, Azure Data Factory, Azure Data Lake, Azure Arc, Azure Security Center, and Azure Databricks.
Hybrid & multicloud support
Run machine learning on existing Kubernetes clusters on premises, in multicloud environments, and at the edge with Azure Arc. Use the simple machine learning agent to start training models more securely, wherever your data lives.
Enterprise-grade security
Build and deploy models more securely with network isolation and end-to-end private IP capabilities, role-based access control for resources and actions, custom roles, and managed identity for compute resources.
Cost Management
Reduce IT costs and better manage resource allocations for compute instances, with workspace and resource-level quota limits and automatic shutdown.
Comprehensive security and compliance,built in
Microsoft invests more than $1 billion annually on cybersecurity research and development.
We employ more than 3,500 security experts who are dedicated to data security and privacy.
Get started with an Azure free account
Start free. Get $200 credit to use within 30 days. While you have your credit, get free amounts of many of our most popular services, plus free amounts of 55+ other services that are always free.
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.
Author new models and store your compute targets, models, deployments, metrics, and run histories in the cloud.
Use automated machine learning to identify algorithms and hyperparameters and track experiments in the cloud. Author models using notebooks or the drag-and-drop designer.
Deploy your machine learning model to the cloud or the edge, monitor performance, and retrain it as needed.
