Azure Machine Learning

name: Azure Machine Learning
description: Azure Machine Learning is an enterprise-grade cloud service for building, training, deploying, and managing machine
  learning models. It supports the full ML lifecycle including data preparation, model training, evaluation, deployment, and
  monitoring with MLOps capabilities.
deliveryModel:
  model: saas
  open_source: false
  commercial: true
  callable_host: true
  label: Hosted service · you call their endpoint
  confidence: high
  source:
  - openapi
  - pricing
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: freemium
  onboarding: self-serve
  trial: false
  try_now: true
  public: false
  gated: none
  gated_source:
  - onboarding
  label: Freemium · Self-serve signup
  confidence: medium
  source:
  - plans
  - authentication
  - security
  generated: '2026-11-06'
  method: derived
image: https://azure.microsoft.com/svghandler/machine-learning/
url: https://azure.microsoft.com/en-us/services/machine-learning/
created: '2026-03-13'
modified: '2026-05-19'
specificationVersion: '0.23'
tags:
- Artificial Intelligence
- Microsoft Azure
- Machine Learning
- MLOps
- Model Deployment
- Model Training
tags_raw:
- AI
- Azure
- Machine Learning
- MLOps
- Model Deployment
- Model Training
- Machine-Learning
apis:
- aid: microsoft-azure-machine-learning:microsoft-azure-machine-learning-operations-api
  name: Azure Machine Learning Operations API
  description: Operations operations
  humanURL: https://learn.microsoft.com/en-us/rest/api/azureml/
  baseURL: https://management.azure.com
  tags: []
  tags_raw:
  - Operations
  properties:
  - type: OpenAPI
    url: openapi/microsoft-azure-machine-learning-operations-api-openapi.yml
  - type: Documentation
    url: https://learn.microsoft.com/en-us/azure/machine-learning/
  - type: APIReference
    url: https://learn.microsoft.com/en-us/rest/api/azureml/
  - type: Authentication
    url: https://learn.microsoft.com/en-us/rest/api/azure/
  - type: GettingStarted
    url: https://learn.microsoft.com/en-us/azure/machine-learning/quickstart-create-resources
  - type: Pricing
    url: https://azure.microsoft.com/en-us/pricing/details/machine-learning/
  - type: SDKs
    url: https://learn.microsoft.com/en-us/python/api/overview/azure/ai-ml-readme
  - type: SDKs
    url: https://learn.microsoft.com/en-us/dotnet/api/overview/azure/resourcemanager.machinelearning-readme
- aid: microsoft-azure-machine-learning:microsoft-azure-machine-learning-workspaces-api
  name: Azure Machine Learning Workspaces API
  description: Workspaces operations
  humanURL: https://learn.microsoft.com/en-us/rest/api/azureml/
  baseURL: https://management.azure.com
  tags:
  - Workspace
  tags_raw:
  - Workspaces
  properties:
  - type: OpenAPI
    url: openapi/microsoft-azure-machine-learning-workspaces-api-openapi.yml
  - type: Documentation
    url: https://learn.microsoft.com/en-us/azure/machine-learning/
  - type: APIReference
    url: https://learn.microsoft.com/en-us/rest/api/azureml/
  - type: Authentication
    url: https://learn.microsoft.com/en-us/rest/api/azure/
  - type: GettingStarted
    url: https://learn.microsoft.com/en-us/azure/machine-learning/quickstart-create-resources
  - type: Pricing
    url: https://azure.microsoft.com/en-us/pricing/details/machine-learning/
  - type: SDKs
    url: https://learn.microsoft.com/en-us/python/api/overview/azure/ai-ml-readme
  - type: SDKs
    url: https://learn.microsoft.com/en-us/dotnet/api/overview/azure/resourcemanager.machinelearning-readme
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
common:
- type: Plans
  url: plans/microsoft-azure-machine-learning-plans-pricing.yml
- type: RateLimits
  url: rate-limits/microsoft-azure-machine-learning-rate-limits.yml
- type: FinOps
  url: finops/microsoft-azure-machine-learning-finops.yml
- type: CapabilityMap
  url: capabilities/microsoft-azure-machine-learning-capability-edges.yml
  name: Azure Machine Learning Business Capability Map
- type: Website
  url: https://www.microsoft.com/
- type: AgenticAccess
  url: agentic-access/microsoft-azure-machine-learning-agentic-access.yml
- type: DomainSecurity
  url: security/microsoft-azure-machine-learning-domain-security.yml
- type: Authentication
  url: authentication/microsoft-azure-machine-learning-authentication.yml
- type: OAuthScopes
  url: scopes/microsoft-azure-machine-learning-scopes.yml
- type: GitHubOrganization
  url: https://github.com/Azure
- type: Portal
  url: https://portal.azure.com/
- type: Documentation
  url: https://learn.microsoft.com/en-us/azure/machine-learning/
- type: Pricing
  url: https://azure.microsoft.com/en-us/pricing/details/machine-learning/
- type: StatusPage
  url: https://status.azure.com/
- type: Blog
  url: https://azure.microsoft.com/en-us/blog/
- type: Support
  url: https://azure.microsoft.com/en-us/support/
- type: TermsOfService
  url: https://azure.microsoft.com/en-us/support/legal/
- type: PrivacyPolicy
  url: https://privacy.microsoft.com/en-us/privacystatement
- type: Features
  data:
  - name: Workspace Management
    description: Create and manage Azure ML workspaces as the top-level resource for ML assets and experiments.
  - name: Compute Resources
    description: Provision and manage compute clusters, compute instances, and Kubernetes-attached compute targets.
  - name: Model Training
    description: Run training jobs at scale with automated ML, distributed training, and hyperparameter tuning.
  - name: Model Deployment
    description: Deploy models as managed online endpoints, batch endpoints, or to Kubernetes for real-time and batch inference.
  - name: MLOps and Pipelines
    description: Build reproducible ML pipelines with versioning, CI/CD integration, and model registry capabilities.
  - name: Responsible AI
    description: Use built-in tools for fairness assessment, interpretability, and model monitoring across the lifecycle.
- type: UseCases
  data:
  - name: Predictive Analytics
    description: Build and deploy predictive models for forecasting, classification, and regression scenarios.
  - name: Computer Vision
    description: Train and deploy image classification, object detection, and segmentation models.
  - name: Natural Language Processing
    description: Build NLP models for text classification, entity recognition, and sentiment analysis.
  - name: MLOps and Production ML
    description: Operationalize ML models with automated training pipelines, deployment, and monitoring.
- type: Integrations
  data:
  - name: Azure Storage
    description: Store training data, models, and experiment artifacts in Azure Blob Storage and Data Lake.
  - name: Azure Kubernetes Service
    description: Deploy ML models to AKS for production-grade inference at scale.
  - name: Azure DevOps
    description: Integrate ML pipelines with Azure DevOps for continuous integration and deployment.
  - name: GitHub Actions
    description: Automate ML workflows with GitHub Actions for training and deployment automation.
  - name: Power BI
    description: Consume ML model predictions in Power BI dashboards and reports.
- type: LlmsText
  url: https://portal.azure.com/llms.txt
x-parent: microsoft-azure
x-relationship: product