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.
Kin Score
APIs 2
Individual APIs this provider publishes, each with its own machine-readable definition.
Azure Machine Learning Operations API
Operations operations
Azure Machine Learning Workspaces API
Workspaces operations
Open Collections 1
Open, tool-agnostic API collections (OpenAPI-derived and Bruno).
Azure Machine Learning REST API
OPEN COLLECTIONPricing Plans 1
Published pricing tiers and plan structures.
Rate Limits 1
Documented rate limits and quota policies.
Microsoft Azure Machine Learning Rate Limits
RATE LIMITSFinOps 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.
Scopes 1
OAuth scopes governing access to this provider's APIs.
Agentic Access 1
Recommended x-agentic-access execution contracts for AI agents.
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