Microsoft Azure Azure ML Web Services Management Client
Browse all Azure APIs★ Only Publicly Available OpenAPI DocumentAI/MLMl Inferenceoauth29 EndpointsREST
For Agents
Deploy, patch, and inspect Azure Machine Learning web services and retrieve their access keys so an agent can manage classic ML inference endpoints programmatically.
Use for: Deploy a new Azure ML web service from a package, I want to retrieve the access keys for an Azure ML web service, List all Azure ML web services in a resource group, Patch the title of a deployed Azure ML web service
Not supported: Does not handle model training, dataset management, or modern Azure ML online endpoints — use for ML Studio (classic) web service deployment, key, and inventory operations only.
Jentic publishes the only available OpenAPI specification for Azure ML Web Services Management Client, keeping it validated and agent-ready. The API manages Azure Machine Learning web services published from ML Studio (classic), supporting create-or-update, get, patch, delete, region-blob creation, and key retrieval under the Microsoft.MachineLearning provider. It is the control surface for deploying inference endpoints, rotating their access keys, and listing every web service in a resource group or subscription.
Install Jentic One Beta
Connect the Azure ML Web Services Management Client to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Azure ML Web Services 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 Azure ML Web Services Management Client API.
Create or update an Azure ML web service with its package, properties, and runtime configuration
Patch a deployed web service to update properties without a full redeploy
Retrieve the primary and secondary access keys for an Azure ML web service
List every Azure ML web service in a subscription or scoped to a resource group
Create a regional blob upload location for staging a web service package before deployment
Delete an Azure ML web service when its inference endpoint is decommissioned
Use Cases
Patterns agents use Azure ML Web Services Management Client API for, with concrete tasks.
★ Deploy an ML Studio Web Service from CI
Trigger an Azure ML web service deployment from a continuous integration pipeline once a new model package has been built. The PUT on /webServices/{webServiceName} accepts the package payload, properties, and the runtime tier, and the operation returns a long-running operation handle so the pipeline can poll until the service is ready. This avoids manual ML Studio publishing and keeps deployments versioned alongside code.
Create or update the Azure ML web service 'fraud-scorer-v3' in resource group 'ml-rg' with the latest package payload and poll until provisioningState is Succeeded
Rotate Web Service Access Keys
Periodically rotate the primary and secondary access keys for an Azure ML web service so that consumer applications can switch between keys with no downtime. The listKeys action returns both keys for a single web service, allowing the caller to update downstream consumers before regenerating. This is critical for SOC 2 and ISO 27001 controls that require regular credential rotation on production inference endpoints.
Call POST listKeys on web service 'fraud-scorer-v3' in resource group 'ml-rg' and return both primary and secondary keys for downstream rotation
Inventory and Tag Web Services for Cost Allocation
List every Azure ML web service in a subscription and patch tags on each so that cost reports can attribute inference spend to the correct cost centre. The subscription-scoped GET returns paginated web services across all resource groups, and PATCH on /webServices/{webServiceName} applies tag updates without modifying the deployed package. This produces a complete inventory in seconds rather than walking resource groups by hand.
List every Azure ML web service in subscription '00000000-0000-0000-0000-000000000000' and patch each one to add the tag costCentre=ml-platform
Agent-Driven ML Endpoint Audit via Jentic
An AI agent uses Jentic to discover every Azure ML web service in a tenant, fetch each endpoint's keys via listKeys, and emit an audit report covering deployment age, location, and access key rotation status. The agent searches Jentic for 'list azure ml web services', loads the schema, and chains the call into per-service listKeys requests. Azure AD bearer tokens are sourced from the Jentic vault per call.
Search Jentic for 'list azure ml web services', list every web service in the subscription, then call listKeys on each and emit an audit report ranking endpoints by last-modified date
Key Endpoints
9 endpoints — jentic publishes the only available openapi specification for azure ml web services management client, keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
PUT
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/webServices/{webServiceName}
Create or update a web service
GET
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/webServices/{webServiceName}
Get a web service
PATCH
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/webServices/{webServiceName}
Patch a web service
DELETE
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/webServices/{webServiceName}
Delete a web service
POST
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/webServices/{webServiceName}/listKeys
List access keys for a web service
GET
/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearning/webServices
List web services in a subscription
POST
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/webServices/{webServiceName}/CreateRegionalBlob
Create a regional blob for package staging
PUT
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/webServices/{webServiceName}
Create or update a web service
GET
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/webServices/{webServiceName}
Get a web service
PATCH
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/webServices/{webServiceName}
Patch a web service
DELETE
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/webServices/{webServiceName}
Delete a web service
POST
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/webServices/{webServiceName}/listKeys
List access keys for a web service
GET
/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearning/webServices
List web services in a subscription
POST
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/webServices/{webServiceName}/CreateRegionalBlob
Create a regional blob for package staging
Why Jentic?
Three things that make agents converge on Jentic-routed access.
Credential isolation
Azure AD client credentials are stored encrypted in the Jentic vault. A short-lived bearer token scoped to https://management.azure.com/ is fetched per call; raw client secrets and refresh tokens never reach the agent context.
Intent-based discovery
Agents search Jentic by intent (e.g., 'list azure ml web services') and receive the matching Microsoft.MachineLearning operation with its full input schema so the agent can call the right endpoint without browsing the ARM reference.
Time to first call
Direct integration with Microsoft.MachineLearning web services: 1-3 days for ARM auth, long-running operation polling, and key handling. Through Jentic: under an hour from search to first successful call.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Azure ML Web Services Management Client API through Jentic.
Why is there no official OpenAPI spec for Azure ML Web Services Management Client?
Microsoft Azure does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Azure ML Web Services 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 Azure ML Web Services Management Client use?
The API uses Azure Active Directory OAuth 2.0 (the azure_auth scheme) with the user_impersonation scope on https://management.azure.com/. Through Jentic, the bearer token is fetched from the Jentic vault and injected at call time so the agent never sees the raw client secret.
How do I retrieve the access keys for a deployed Azure ML web service?
POST to /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/webServices/{webServiceName}/listKeys. The response contains both primary and secondary keys, which you can rotate by updating consumers and then regenerating one key at a time.
What are the rate limits for the Azure ML Web Services Management Client?
The spec does not declare per-API limits; calls are throttled by Azure Resource Manager's per-subscription budget for read and write operations on Microsoft.MachineLearning. Honour the Retry-After header on HTTP 429 before retrying.
How do I deploy a new ML web service through Jentic?
Search Jentic for 'deploy an azure ml web service', load the schema for PUT /webServices/{webServiceName}, supply your package payload, and execute. Jentic injects the Azure AD bearer token automatically so the agent only handles the deployment payload.
Is the Azure ML Web Services API free?
Control plane calls are free. The deployed web services bill against either pay-as-you-go transactions and compute or against an attached Azure ML commitment plan; both are charged at standard Azure ML rates.
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