# Azure Machine Learning

**Canonical:** https://apis.io/providers/microsoft-azure-machine-learning/  
**Website:** https://portal.azure.com/  
**APIs profiled:** 2

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.

## Kin Score — 40.7 / 100 (developing)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 40.7).

| Facet | Score |
|---|---|
| Discoverability | 75.9 |
| Contract Quality | 50.3 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 26.3 |
| Developer Ergonomics | 38.1 |
| Commercial Clarity | 47.4 |
| Access Clarity | 47.4 |

## Agent readiness — 29.1 (agent-aware)

| Dimension | Value |
|---|---|
| Spec Presence | yes |
| Agentic Access | derived |
| Reversibility Documented | no |
| MCP Server | no |
| Auth Clarity | yes |
| Idempotency | no |
| Error Semantics | no |
| OpenAPI Examples | no |
| Rate Limit Signal | documented |
| Event Surface Described | no |
| Agent Skills | no |
| Well Known Catalog | no |
| Consent Identity | no |
| Agent Card | no |
| Dry Run Mode | no |

## Access

Freemium · Self-serve signup — onboarding: self-serve, pricing: freemium, trial: no (confidence: high).

## APIs (2)

- **Azure Machine Learning Operations API** — Operations operations
- **Azure Machine Learning Workspaces API** — Workspaces operations

## Agentic access (1)

- **Microsoft Azure Machine Learning Agentic Access** — 7 operations · 3 acting

## Security (2)

- **Microsoft Azure Machine Learning Authentication** — oauth2 · 1 scheme
- **Microsoft Azure Machine Learning Domain Security** — TLSv1.3 · HSTS · DMARC

## Plans (1)

- **Microsoft Azure Machine Learning Plans Pricing**

## Use cases (4)

- **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.

## Tags

Artificial Intelligence, Azure, Machine-Learning, MLOps, Model Deployment, Model Training

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Profiled by [API Evangelist](https://apievangelist.com) and published on [APIs.io](https://apis.io/providers/microsoft-azure-machine-learning/). Scores are computed from the provider's own public artifacts under a published rubric.