Azure ML Web Services Management Client

Name: Azure ML Web Services Management Client API
Brand: Azure ML Web Services Management Client
Availability: InStock

★ 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.

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.

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 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

GET STARTED

Start building with Azure ML Web Services Management Client API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI document for Azure ML Web Services Management Client, keeping it validated and agent-ready.

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.

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

Why though 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.

Complementary

Azure ML Commitment Plans Management Client

Manages the commitment plans that web services attach to for committed-capacity pricing.

Use alongside this API when the agent needs to bind a deployed web service to committed capacity rather than pay-as-you-go billing.

Alternative

Azure Machine Learning Workspaces

Modern Azure ML workspaces API — the successor surface for current Azure ML deployments.

Choose this for current Azure ML workspaces, online endpoints, and registered models; web services are the legacy ML Studio (classic) deployment surface.

Complementary

Azure Machine Learning Model Management Service

Registers and versions models that can be deployed as web services.

Use to register and version a model artefact before publishing it through the web services API.

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.