Microsoft Azure ML Team Account Management Client
Browse all Azure APIs★ Only Publicly Available OpenAPI DocumentAI/MLMl Inferenceoauth217 EndpointsREST
For Agents
Manage Azure ML Workbench team accounts, workspaces, and projects across 17 control-plane endpoints under Microsoft.MachineLearningExperimentation.
Use for: I need to create an ML team account in eastus2, List all ML workspaces under team account contoso-ml, Provision a new project named churn-model inside workspace marketing-ml, Update the tags on an ML team account to mark it as deprecated
Not supported: Does not handle model training, model deployment, or modern Azure Machine Learning workspaces — use for the legacy ML Workbench team account hierarchy only.
Jentic publishes the only available OpenAPI specification for Azure ML Team Account Management, keeping it validated and agent-ready. The Microsoft.MachineLearningExperimentation Resource Provider manages the legacy Team Accounts hierarchy of Azure Machine Learning Workbench: an account contains workspaces, and each workspace contains projects that hold experiment runs and assets. The control plane exposed here covers full lifecycle for accounts, workspaces, and projects, including SKU and tag updates. Although Workbench has been superseded by the modern Azure Machine Learning Workspace, organizations still operating on this preview can drive provisioning, RBAC scoping, and decommissioning entirely through the API.
Install Jentic One Beta
Connect the ML Team Account Management Client to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the ML Team Account 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 ML Team Account Management Client API.
Create and update ML team accounts in a chosen Azure region with a SKU
List ML team accounts at the subscription or resource group scope
Provision workspaces inside an ML team account for a specific project group
Create projects inside a workspace to hold experiment runs and assets
Update tags and friendly names on accounts, workspaces, and projects in place
Delete projects, workspaces, or whole team accounts during decommissioning
Expose Microsoft.MachineLearningExperimentation operations metadata for tooling
Use Cases
Patterns agents use ML Team Account Management Client API for, with concrete tasks.
★ Onboarding a new data science team
Platform teams that still operate on the ML Workbench preview create one team account per business unit, then provision a workspace for each data science squad and a project per modelling initiative. The API performs the full hierarchy creation in a single workflow, applies tags for cost allocation, and grants RBAC roles via the resource manager. Onboarding a new team takes minutes rather than the manual portal walkthrough.
Create ML team account churn-team in eastus2, create workspace marketing-ml inside it, then create project churn-q3 inside that workspace, all in resource group ml-prod.
Decommissioning legacy Workbench environments
As organizations migrate from ML Workbench to the modern Azure Machine Learning workspace, they need a clean way to inventory and tear down the legacy hierarchy. Listing accounts, then walking down to workspaces and projects, gives the full set of resources to decommission. The API supports DELETE on each level, allowing scripted teardown after final artifact export.
List all projects under workspace experiments-old, export their metadata to a JSON manifest, then delete each project followed by the workspace and the parent ML team account.
Cost allocation across ML team accounts
Finance teams allocating cloud spend across ML projects need consistent tags on every account, workspace, and project. The PATCH operations on each resource type accept tag updates without recreating the resource, allowing a governance agent to enforce tag policy across hundreds of objects in a single pass.
Apply the tag costCenter=DS-204 to ML team account contoso-ml and to every workspace and project beneath it, using PATCH on each resource.
Agent-driven ML hierarchy management
AI agents can use Jentic to discover Microsoft.MachineLearningExperimentation operations by intent, load the structured input schema, and execute calls against management.azure.com without browsing the Azure REST docs. A platform agent handling new project requests can stand up an entire account-workspace-project chain from a single natural language request and report the resulting ARM IDs back to the requester.
Search Jentic for create azure ml team account, load the input schema, and execute it for a new account in westus2 with the SKU F1Basic and tag environment=dev.
Key Endpoints
17 endpoints — jentic publishes the only available openapi specification for azure ml team account management, keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
PUT
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningExperimentation/accounts/{accountName}
Create or update an ML team account
GET
/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningExperimentation/accounts
List ML team accounts in a subscription
PUT
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningExperimentation/accounts/{accountName}/workspaces/{workspaceName}
Create or update a workspace under an ML team account
PUT
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningExperimentation/accounts/{accountName}/workspaces/{workspaceName}/projects/{projectName}
Create or update a project under a workspace
DELETE
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningExperimentation/accounts/{accountName}
Delete an ML team account and all child resources
PUT
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningExperimentation/accounts/{accountName}
Create or update an ML team account
GET
/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningExperimentation/accounts
List ML team accounts in a subscription
PUT
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningExperimentation/accounts/{accountName}/workspaces/{workspaceName}
Create or update a workspace under an ML team account
PUT
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningExperimentation/accounts/{accountName}/workspaces/{workspaceName}/projects/{projectName}
Create or update a project under a workspace
DELETE
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningExperimentation/accounts/{accountName}
Delete an ML team account and all child resources
Why Jentic?
Three things that make agents converge on Jentic-routed access.
Credential isolation
Azure AD OAuth credentials and service principals are stored encrypted in the Jentic vault. Agents receive scoped bearer tokens for management.azure.com at execution time; client secrets and certificates never enter the agent context.
Intent-based discovery
Agents search by intent (for example create azure ml team account or list azure ml workspaces) and Jentic returns the matching ARM operation with its full input schema, so the agent can call the correct path without browsing Microsoft Learn docs.
Time to first call
Direct integration with the Microsoft.MachineLearningExperimentation REST API: 1-2 days for AAD auth, three-tier hierarchy modeling, and tag handling. Through Jentic: under 1 hour to search, load, and execute.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using ML Team Account Management Client API through Jentic.
Why is there no official OpenAPI spec for ML Team Account Management?
Microsoft Azure does not publish an OpenAPI specification for the Microsoft.MachineLearningExperimentation Resource Provider. Jentic generates and maintains this spec from the Azure Resource Manager contracts so that AI agents and developers can call ML Team Account Management 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 ML Team Account Management API use?
The API uses Azure Active Directory OAuth 2.0 with the user_impersonation scope against https://login.microsoftonline.com/common/oauth2/authorize. Jentic stores the AAD client secret or service principal credentials encrypted in its vault and supplies scoped bearer tokens to agents at execution time without exposing the underlying credential.
Can I create a new ML team account through this API?
Yes. PUT /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningExperimentation/accounts/{accountName} creates or updates a team account. The body must include a location and SKU and may include tags. Workspaces and projects are then created as child resources under the account.
What are the rate limits for the ML Team Account Management API?
The API follows the standard Azure Resource Manager throttling limits, which apply per subscription and per region. Read operations are limited to 1200 requests per hour per subscription. Bulk provisioning of many accounts in parallel should be paced to avoid 429 responses on the ARM control plane.
How do I create a project inside a workspace through Jentic?
Search Jentic for create azure ml project, load the schema for PUT under accounts/{accountName}/workspaces/{workspaceName}/projects/{projectName}, and execute it with the account, workspace, and project names plus a friendly name and tags. Jentic handles the AAD token exchange and returns the project resource.
Is ML Workbench still supported by Microsoft?
Microsoft has retired the Azure Machine Learning Workbench client tooling, but the underlying Microsoft.MachineLearningExperimentation control plane remains reachable for organizations that still hold these resources. Net-new ML projects should use the modern Azure Machine Learning workspace under Microsoft.MachineLearningServices.
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