Azure Machine Learning Workspaces

Name: Azure Machine Learning Workspaces API
Brand: Azure Machine Learning Workspaces
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLMl Inferenceoauth220 EndpointsREST

For Agents

Provision Azure Machine Learning workspaces and their compute targets, manage region quotas and VM sizes, and read or rotate the workspace's storage and registry keys.

Connect the Azure Machine Learning Workspaces to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Azure Machine Learning Workspaces, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Install Jentic One Beta (Self-Hosted)

Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh

Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with Azure Machine Learning Workspaces API.

Provision a Microsoft.MachineLearningServices workspace inside a resource group

Attach a compute target such as AKS, AmlCompute, or ComputeInstance to a workspace

List every workspace across a subscription or scoped to a resource group

Resync the storage and container registry keys backing a workspace

GET STARTED

Start building with Azure Machine Learning Workspaces API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI document for Azure Machine Learning Workspaces, keeping it validated and agent-ready.

Jentic publishes the only available OpenAPI specification for Azure Machine Learning Workspaces, keeping it validated and agent-ready. This API manages the Microsoft.MachineLearningServices/workspaces resource and the compute targets attached to it. Operators can provision a workspace, list workspaces across subscriptions or resource groups, attach compute clusters or compute instances, list available VM sizes, manage region quotas, and read or resync the workspace's storage and registry keys. All requests authenticate with Azure AD OAuth 2.0 against Azure Resource Manager.

Use Cases

Patterns agents use Azure Machine Learning Workspaces API for, with concrete tasks.

★ Workspace and Compute Bootstrap

Platform teams provision a Machine Learning workspace and its dependent compute targets in one automated run. The PUT on /workspaces/{workspaceName} creates the workspace with a managed storage account, key vault, container registry, and Application Insights component, and the PUT on /computes/{computeName} attaches an AmlCompute or AKS target for training and inference. Together these calls stand up an ML environment ready for model registration and deployment.

Create workspace 'ml-prod' in resource group 'rg-ml' in West Europe with a system-assigned managed identity, then attach an AmlCompute cluster named 'cpu-cluster' with VM size 'Standard_DS3_v2' and minNodeCount=0, maxNodeCount=4.

Quota Management Across Regions

AML compute is bounded by per-region quotas on cores per VM family. The GET on /locations/{location}/Quotas returns the current allocation, /usages reports actual consumption, and the POST on /updateQuotas raises or lowers the limit per VM family. Platform teams use this trio to track headroom before launching large training jobs and to request increases when usage approaches the cap.

Read the current AmlCompute quota for 'westeurope' and update the 'standardDSv2Family' quota from 24 to 96 cores.

Storage Key Resync After Rotation

Each AML workspace ships with a managed storage account and container registry whose keys can rotate at the storage layer. The POST on /workspaces/{workspaceName}/resyncKeys forces the workspace to re-read the latest keys, restoring jobs and pipelines that broke when the underlying credentials changed. Use this after rotating storage account keys outside of AML.

Resync keys on workspace 'ml-prod' in resource group 'rg-ml' after rotating the underlying storage account's primary key, and confirm via listKeys.

Agent-Driven Workspace Inventory

An AI agent through Jentic can enumerate every Machine Learning workspace across subscriptions, list each workspace's attached compute, and queue actions like quota updates or compute resizing. Jentic returns the operation schema for each call so the agent can drive AML control-plane work end to end without reading the AML SDK reference.

List every workspace in subscription '00000000-0000-0000-0000-000000000000', and for each, list attached computes whose vmSize is 'Standard_NC24'.

Key Endpoints

20 endpoints — jentic publishes the only available openapi specification for azure machine learning workspaces, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

PUT

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}

Create or update an AML workspace

PUT

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}

Attach a compute target

GET

/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/workspaces

List workspaces in a subscription

POST

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/listKeys

List workspace storage and registry keys

POST

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/resyncKeys

Resync workspace dependent keys

POST

/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/locations/{location}/updateQuotas

Update region AML quotas

GET

/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/locations/{location}/vmSizes

List supported AML VM sizes in a region

PUT

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}

Create or update an AML workspace

PUT

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}

Attach a compute target

GET

/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningServices/workspaces

List workspaces in a subscription

POST

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/listKeys

List workspace storage and registry keys

POST

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/resyncKeys

Why though Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

Azure AD service principal credentials and resynced workspace keys are stored encrypted in the Jentic vault. Agents receive short-lived Bearer tokens scoped to https://management.azure.com/ — client secrets and storage account keys never enter the agent context.

Intent-based discovery

Agents search by intent (e.g., 'create an Azure ML workspace' or 'attach AmlCompute') and Jentic returns the matching Microsoft.MachineLearningServices operation with its parameter schema.

Time to first call

Direct integration: 2-3 days for Azure AD setup, ARM polling, compute attachment, and quota wiring. Through Jentic: under 2 hours — search, load schema, execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Complementary

Azure Machine Learning Model Management Service

Manages models, images, and deployed inference services inside the workspace provisioned here.

Use after the workspace is provisioned to register and deploy models on its attached compute.

Complementary

Azure Machine Learning Datastore Management Client

Registers datastores against the workspace so jobs can mount training data.

Use after the workspace exists to connect storage accounts and data lakes for jobs.

Alternative

Azure Machine Learning Compute Management Client

Newer compute-focused control surface that overlaps with the compute endpoints exposed here.

Choose when you only need compute lifecycle and not the full workspace control plane.

FAQs

Specific to using Azure Machine Learning Workspaces API through Jentic.

Why is there no official OpenAPI spec for Azure Machine Learning Workspaces?

Microsoft Azure does not publish a single consolidated OpenAPI specification for the Microsoft.MachineLearningServices workspace control plane. Jentic generates and maintains this spec so that AI agents and developers can call Azure Machine Learning Workspaces via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.

What authentication does the Azure Machine Learning Workspaces API use?

The API uses Azure Active Directory OAuth 2.0; agents acquire a token for the https://management.azure.com/ resource and pass it as a Bearer token. Jentic stores the service principal credentials in its vault and injects scoped tokens at call time.

Can I attach an AKS cluster to a workspace through this API?

Yes. The PUT on /workspaces/{workspaceName}/computes/{computeName} accepts a properties block whose computeType field can be AKS, AmlCompute, ComputeInstance, DataFactory, Databricks, or VirtualMachine. AKS attachment expects an existing cluster's resource ID.

How do I provision a workspace through Jentic?

Search Jentic for 'create an Azure Machine Learning workspace', load the resulting PUT operation on /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}, then execute it with the workspace name, location, and identity block. Install with pip install jentic and run the async search-load-execute flow.

What is the difference between listKeys and resyncKeys?

listKeys returns the current values of the workspace's underlying storage and container registry keys; resyncKeys forces the workspace to re-read those values after they have rotated at the storage layer. Use resyncKeys when jobs start failing with auth errors after a key rotation.

What are the rate limits for this API?

Azure Resource Manager applies subscription-level throttling — typically 12,000 reads and 1,200 writes per hour per subscription. Workspace and compute provisioning are long-running and return 202 with an Azure-AsyncOperation header for polling.