Microsoft Azure Machine Learning Workspaces Management Client
Browse all Azure APIs★ Only Publicly Available OpenAPI DocumentAI/MLMl Inferenceoauth29 EndpointsREST
For Agents
Provision, list, and manage classic Azure ML Studio workspaces and their access keys through Azure Resource Manager. Use for workspace lifecycle, not for running experiments or modern Azure ML v2.
Use for: Provision a classic Azure ML Studio workspace in westeurope, List all Microsoft.MachineLearning workspaces in my subscription, Retrieve the storage account keys for the 'demos-ml' workspace, Resync workspace keys after a storage account credential rotation
Not supported: Does not run experiments, manage compute, or operate Azure ML v2 (Microsoft.MachineLearningServices) — use only for classic Microsoft.MachineLearning workspace lifecycle and key operations.
Jentic publishes the only available OpenAPI specification for Machine Learning Workspaces Management Client, keeping it validated and agent-ready. This API manages Microsoft.MachineLearning workspaces — the legacy 'classic' Azure Machine Learning Studio workspace resource, which contains experiments, datasets, and web service plans. It exposes workspace provisioning, key listing, and resync via Azure Resource Manager. It does not run experiments or manage Azure Machine Learning v2 workspaces.
Install Jentic One Beta
Connect the Machine Learning Workspaces Management Client to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Machine Learning Workspaces Management Client, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.
Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.
1
Step 1: 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 | sh2
Step 2: 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 instanceJentic 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 Machine Learning Workspaces Management Client API.
Provision and delete classic Azure Machine Learning Studio workspaces
List Microsoft.MachineLearning workspaces across a subscription or resource group
Retrieve and resynchronise workspace storage account keys
Update workspace tags and properties through PATCH
Inspect the operations catalog exposed by the Microsoft.MachineLearning provider
Use Cases
Patterns agents use Machine Learning Workspaces Management Client API for, with concrete tasks.
★ Classic ML Studio Workspace Provisioning
Data science teams running on Azure ML Studio (classic) provision new workspaces tied to a storage account. Workspaces_CreateOrUpdate (PUT on /workspaces/{workspaceName}) accepts a SKU, location, and ownerEmail, and returns the workspace resource. The ARM call is synchronous for classic workspaces, so the resource is ready immediately for downstream Studio access.
PUT a workspace named 'demos-ml' in westeurope with ownerEmail 'team@example.com' and a Standard SKU to /subscriptions/{subscriptionId}/resourceGroups/ml-prod/providers/Microsoft.MachineLearning/workspaces/demos-ml.
Workspace Key Rotation and Resync
When a storage account behind a classic ML workspace has its keys rotated, the workspace's cached credential must be refreshed. ResyncStorageKeys forces the workspace to pull the current key from the linked storage account, restoring access for experiments without requiring a workspace rebuild.
Call POST /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/workspaces/{workspaceName}/resyncStorageKeys for each workspace bound to the rotated storage account.
Subscription Inventory and Decommission Planning
Platform owners migrating off classic Azure ML Studio inventory every Microsoft.MachineLearning workspace in the subscription, then decide which to decommission. Workspaces_List returns the full set with creation date and SKU, and Workspaces_Delete removes the resource cleanly. Combined with tagging conventions, this drives a controlled migration to Azure ML v2.
List all Microsoft.MachineLearning workspaces in the subscription created before 2024, return name and resource group, and DELETE any tagged decom=true.
AI Agent Workspace Operations
Maintenance agents that own a portfolio of classic ML workspaces use Jentic to find the right ARM operation on demand. Searching for a workspace intent returns the matching schema; the agent loads it and executes against management.azure.com with a token retrieved from the Jentic vault — no SDK install required.
Use Jentic to search 'list azure ml studio workspaces', load the Workspaces_List operation, and execute against the target subscription, returning workspace name and SKU per resource group.
Key Endpoints
9 endpoints — jentic publishes the only available openapi specification for machine learning workspaces management client, keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
PUT
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/workspaces/{workspaceName}
Create or update a classic ML workspace
GET
/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearning/workspaces
List ML workspaces in a subscription
DELETE
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/workspaces/{workspaceName}
Delete a classic ML workspace
POST
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/workspaces/{workspaceName}/listWorkspaceKeys
List workspace storage keys
PATCH
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/workspaces/{workspaceName}
Update workspace tags or properties
PUT
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/workspaces/{workspaceName}
Create or update a classic ML workspace
GET
/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearning/workspaces
List ML workspaces in a subscription
DELETE
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/workspaces/{workspaceName}
Delete a classic ML workspace
POST
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/workspaces/{workspaceName}/listWorkspaceKeys
List workspace storage keys
PATCH
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/workspaces/{workspaceName}
Update workspace tags or properties
Why Jentic?
Three things that make agents converge on Jentic-routed access.
Credential isolation
AAD client credentials are stored encrypted in the Jentic vault. Agents receive only short-lived bearer tokens scoped to https://management.azure.com/ at call time. Workspace storage keys returned by ListWorkspaceKeys are surfaced through the vault rather than agent logs.
Intent-based discovery
Agents search by intent (for example, 'list azure ml studio workspaces') and Jentic returns the Workspaces_List operation with its parameter schema, so the agent calls the correct endpoint without consulting Microsoft Docs.
Time to first call
Direct ARM integration: about 1 day for AAD setup and key handling. Through Jentic: under 30 minutes — search, load schema, execute.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Machine Learning Workspaces Management Client API through Jentic.
Why is there no official OpenAPI spec for Machine Learning Workspaces Management Client?
Microsoft Azure does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Machine Learning Workspaces Management Client 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 Machine Learning Workspaces Management Client use?
AAD OAuth 2.0 bearer tokens scoped to https://management.azure.com/. Through Jentic, AAD client credentials live in the encrypted vault and a short-lived bearer token is injected at call time.
Does this API manage Azure ML v2 workspaces?
No. This API manages the classic Microsoft.MachineLearning provider (Azure ML Studio classic). The current Azure Machine Learning service uses the Microsoft.MachineLearningServices provider, which exposes a different workspace resource with online endpoints, compute clusters, and pipelines.
What are the rate limits for the Machine Learning Workspaces Management Client?
Standard Azure Resource Manager throttling: typically 12,000 reads and 1,200 writes per hour per subscription per region. Workspace create and delete are typically synchronous for classic workspaces.
How do I rotate workspace storage keys through Jentic?
Run pip install jentic, then search Jentic for 'resync azure ml workspace storage keys'. Jentic returns the Workspaces_ResyncStorageKeys operation; load it, supply the workspace path, and execute the POST against /workspaces/{workspaceName}/resyncStorageKeys.
Can I run experiments or train models with this API?
No. Experiment, dataset, and web service operations live in the ML Studio data plane (or in Azure ML v2 for modern workloads). This client is the ARM control plane for the workspace resource only.
GET STARTED