Machine Learning Workspaces Management Client

Name: Machine Learning Workspaces Management Client API
Brand: Machine Learning Workspaces Management Client
Availability: InStock

★ 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.

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.

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 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

GET STARTED

Start building with Machine Learning Workspaces Management Client API

Explore with Jentic

View OpenAPI Document

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

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.

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