Azure Machine Learning website screenshot

Azure Machine Learning

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.

Azure Machine Learning publishes 2 APIs on the APIs.io network: Operations API and Workspaces API. Tagged areas include AI, Azure, Machine Learning, MLOps, and Model Deployment.

Azure Machine Learning’s developer surface includes authentication, developer portal, documentation, pricing, engineering blog, support, and 8 more developer resources.

49.4/100 developing ▬ flat Agent 31/100 agent aware Full breakdown ↓
scored 2026-07-28 · rubric v0.6
AccessFreemiumSelf serve⚡ Free to try
2 APIs 6 Features 4 Use Cases
AIAzureMachine LearningMLOpsModel DeploymentModel Training

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-07-28 · rubric v0.6
Composite quality — 49.4/100 · developing
Contract Quality 13.8 / 25
Developer Ergonomics 7.0 / 20
Commercial Clarity 14.2 / 20
Operational Transparency 6.8 / 13
Governance 0.0 / 12
Discoverability 7.6 / 10
Agent readiness — 31/100 · agent aware
Machine-Readable Contract 18 / 18
Agentic Access Contract 10 / 10
MCP Server 0 / 12
Machine-Readable Auth 10 / 10
Idempotency 0 / 9
Stable Error Semantics 0 / 8
Request/Response Examples 0 / 7
Rate-Limit Signaling 7 / 7
Typed Event Surface 0 / 6
Agent Skills 0 / 5
Well-Known Catalog 0 / 4
Consent & Bot Identity 0 / 3
A2A Agent Card 0 / 8
Dry-Run / Simulate Mode 0 / 4
Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. This rating is computed from github.com/api-evangelist/microsoft-azure-machine-learning: open an issue to ask a question, or submit a pull request to add artifacts. Want it done for you? Prioritized profiling — $2,500 →

APIs 2

Individual APIs this provider publishes, each with its own machine-readable definition.

Open Collections 1

Open, tool-agnostic API collections (OpenAPI-derived and Bruno).

Pricing Plans 1

Published pricing tiers and plan structures.

Rate Limits 1

Documented rate limits and quota policies.

FinOps 1

Cost, billing, and metering signals for API financial operations.

Features 6

Notable capabilities this provider offers.

Workspace Management

Create and manage Azure ML workspaces as the top-level resource for ML assets and experiments.

Compute Resources

Provision and manage compute clusters, compute instances, and Kubernetes-attached compute targets.

Model Training

Run training jobs at scale with automated ML, distributed training, and hyperparameter tuning.

Model Deployment

Deploy models as managed online endpoints, batch endpoints, or to Kubernetes for real-time and batch inference.

MLOps and Pipelines

Build reproducible ML pipelines with versioning, CI/CD integration, and model registry capabilities.

Responsible AI

Use built-in tools for fairness assessment, interpretability, and model monitoring across the lifecycle.

Security Posture 2

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Microsoft Azure Machine Learning Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Scopes 1

OAuth scopes governing access to this provider's APIs.

Microsoft Azure Machine Learning Scopes

1 scope · implicit

1 scopes

SCOPES

Agentic Access 1

Recommended x-agentic-access execution contracts for AI agents.

Microsoft Azure Machine Learning Agentic Access

7 operations · 3 acting

7 operations · 3 acting

AGENTIC

Use Cases 4

What developers build with this provider.

Predictive Analytics

Build and deploy predictive models for forecasting, classification, and regression scenarios.

Computer Vision

Train and deploy image classification, object detection, and segmentation models.

Natural Language Processing

Build NLP models for text classification, entity recognition, and sentiment analysis.

MLOps and Production ML

Operationalize ML models with automated training pipelines, deployment, and monitoring.

Integrations 5

Pre-built integrations with other platforms and tools.

Azure Storage

Store training data, models, and experiment artifacts in Azure Blob Storage and Data Lake.

Azure Kubernetes Service

Deploy ML models to AKS for production-grade inference at scale.

Azure DevOps

Integrate ML pipelines with Azure DevOps for continuous integration and deployment.

GitHub Actions

Automate ML workflows with GitHub Actions for training and deployment automation.

Power BI

Consume ML model predictions in Power BI dashboards and reports.

Resources

Get Started 1

Portal, sign-up, and the first successful call

Documentation 1

Reference material describing how the API behaves

Agent Surfaces 2

MCP servers, agent skills, and machine-readable catalogs

Build 1

SDKs, sample code, and the tooling you integrate with

Access & Security 3

Authentication, authorization, and security posture

Operate 2

Status, limits, changes, and where to get help

Commercial 3

Pricing, plans, and the legal terms of use

Company 1

The organization behind the API

Source (apis.yml)

apis.yml Raw ↑
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.
accessModel:
  pricing: freemium
  onboarding: self-serve
  trial: false
  try_now: true
  public: false
  label: Freemium · Self-serve signup
  confidence: high
  source:
  - plans
  - authentication
  generated: '2026-07-22'
  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: '0.18'
tags:
- AI
- Azure
- Machine Learning
- MLOps
- Model Deployment
- Model Training
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:
  - 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:
  - 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: 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