Microsoft Azure Azure Machine Learning Model Management Service

Name: Azure Machine Learning Model Management Service API
Brand: Azure Machine Learning Model Management Service
Availability: InStock

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★ Only Publicly Available OpenAPI DocumentAI/MLMl Inferenceoauth223 Endpoints

Install Jentic One Beta

Connect the Azure Machine Learning Model Management Service to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Azure Machine Learning Model Management Service, 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.

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 | sh

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 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 Machine Learning Model Management Service API.

List models in a workspace filtered by tag, name, or framework

Build a container image from a registered model and an inference config

GET STARTED

Start building with Azure Machine Learning Model Management Service API

Explore with Jentic

View OpenAPI Document

For Agents

Register Azure Machine Learning models, build container images, profile resource requirements, deploy inference services, and rotate scoring keys or bearer tokens for service callers.

Use for: I need to register a trained model with an Azure ML workspace, List all models registered to a workspace, Deploy a registered model as a real-time inference service, Profile a model image for resource sizing recommendations

Not supported: Does not train models, manage compute clusters, or move data - use for model registration, image build, profiling, and inference service lifecycle only.

Use Cases

Patterns agents use Azure Machine Learning Model Management Service API for, with concrete tasks.

★ End-to-End Model Registration and Deployment

Data science teams register the trained model artifact, build an image around it with their inference script, and deploy that image as a real-time service. The POST on /models registers the artifact, POST on /assets uploads supporting files, POST on /services deploys the resulting image, and the deployed service returns a scoring URI plus listKeys for caller authentication. The full sequence is scriptable end to end without leaving this API surface.

Register model 'sentiment-classifier' v3 on workspace 'ml-prod', deploy it as a service named 'sentiment-svc' on attached AKS compute, and return the scoringUri and primary key.

Resource Profiling Before Production

The POST on /images/{imageId}/profiles runs the image through a profiling job and returns recommended CPU, memory, and concurrency settings, plus measured latency at different load levels. Teams use this output to size production deployments and avoid over- or under-provisioning AKS or ACI targets.

Create a profile for image 'img-sentiment-v3' on workspace 'ml-prod' with name 'profile-prod' against test dataset 'profile-input', poll the operation, and return the recommended cpuCores and memoryInGB.

Scoring Key Rotation

Deployed inference services authenticate callers with primary and secondary scoring keys, and operators rotate them on a schedule for compliance. The POST on /services/{id}/regenerateKeys accepts 'Primary' or 'Secondary' and issues a fresh value while leaving the other key intact, so clients can cut over without downtime. Pair with /services/{id}/listkeys to retrieve the new value.

Regenerate the secondary key on service 'sentiment-svc' in workspace 'ml-prod' and store the new value in Key Vault secret 'sentiment-secondary-key'.

Token-Based Service Access for Agents

An AI agent through Jentic can call POST /services/{id}/token to get a short-lived bearer token instead of a long-lived scoring key. This is preferred for agent-driven inference because tokens expire and reduce blast radius if leaked. Jentic returns the token operation alongside the scoring URI so the agent can call the service end to end.

Issue a fresh access token for service 'sentiment-svc' in workspace 'ml-prod' and return the token plus the service scoringUri.

Key Endpoints

23 endpoints — jentic publishes the only available openapi specification for azure machine learning model management service, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

POST

/modelmanagement/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/Microsoft.MachineLearningServices/workspaces/{workspace}/models

GET

/modelmanagement/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/Microsoft.MachineLearningServices/workspaces/{workspace}/models

List models in a workspace

POST

/modelmanagement/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/Microsoft.MachineLearningServices/workspaces/{workspace}/services

Deploy a real-time inference service

POST

/modelmanagement/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/Microsoft.MachineLearningServices/workspaces/{workspace}/services/{id}/listkeys

List scoring keys for a service

POST

/modelmanagement/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/Microsoft.MachineLearningServices/workspaces/{workspace}/services/{id}/regenerateKeys

Regenerate a service scoring key

POST

/modelmanagement/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/Microsoft.MachineLearningServices/workspaces/{workspace}/services/{id}/token

Issue a short-lived service token

POST

/modelmanagement/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/Microsoft.MachineLearningServices/workspaces/{workspace}/images/{imageId}/profiles

Profile an image for resource sizing

POST

/modelmanagement/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/Microsoft.MachineLearningServices/workspaces/{workspace}/models

GET

/modelmanagement/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/Microsoft.MachineLearningServices/workspaces/{workspace}/models

List models in a workspace

POST

/modelmanagement/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/Microsoft.MachineLearningServices/workspaces/{workspace}/services

Deploy a real-time inference service

POST

/modelmanagement/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/Microsoft.MachineLearningServices/workspaces/{workspace}/services/{id}/listkeys

List scoring keys for a service

Why Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

Your Azure AD service principal credentials, along with the scoring keys and service tokens this API returns, are stored once, encrypted, by your own Jentic One instance and injected at execution time. They never enter the agent's prompt, logs, or context.

Intent-based discovery

Agents search Jentic by intent such as 'deploy an Azure ML model as a real-time service' or 'issue a short-lived token for a scoring service', and Jentic returns the matching model management operation with its input schema so the agent calls the right endpoint without reading the AML docs.

Time to first call

FAQs

Specific to using Azure Machine Learning Model Management Service API through Jentic.

Why is there no official OpenAPI spec for Azure Machine Learning Model Management Service?

Microsoft Azure does not publish a single consolidated OpenAPI specification for the AML model management control plane. Jentic generates and maintains this spec so that AI agents and developers can call Azure Machine Learning Model Management Service via structured tooling. It is validated against the live API and kept up to date. Get started with Jentic One, the self-hosted execution layer.

What authentication does this API use?

The API uses Azure Active Directory OAuth 2.0; agents acquire a token for the Azure Machine Learning resource and pass it as a Bearer token on every call. Jentic stores the service principal credentials in its vault and injects scoped tokens at call time.

Can I deploy a model to AKS or ACI through this API?

Yes. The POST on /workspaces/{workspace}/services accepts a compute target reference and a deployment config that targets either an attached AKS cluster or an Azure Container Instance. The deployment is asynchronous and the operations endpoint returns the final state and scoringUri.

How do I rotate scoring keys through Jentic?

Search Jentic for 'rotate Azure ML service key', load the resulting POST operation on /workspaces/{workspace}/services/{id}/regenerateKeys, then execute it with keyType set to 'Primary' or 'Secondary'. Read the new value with the listkeys POST and store it in Key Vault.

What is the difference between scoring keys and a service token?

Scoring keys are long-lived primary and secondary values returned by listkeys; tokens issued by /services/{id}/token are short-lived bearer credentials suited to ephemeral callers like agents. Tokens reduce blast radius if leaked, while keys are simpler for static integrations.

What are the rate limits for this API?

Azure Machine Learning applies workspace-level throttling on control-plane operations rather than per-endpoint quotas. Long-running calls like image build and service deploy return 202 with an operation ID you poll via /operations/{id}.

Can I limit what my agent is allowed to do with the Azure Machine Learning Model Management Service API?

Yes. Because you run Jentic One yourself, your own rules decide which operations and credentials the agent may use. The subscription, resource group, workspace, and service id all live in the URL path, so a rule can pin the agent to models and services on a single workspace. You also choose which operations it may call, so sensitive ones like regenerateKeys or deleting a model are excluded unless you explicitly add them.

Microsoft Azure Azure Machine Learning Model Management Service

Connect the Azure Machine Learning Model Management Service to your agent

Capabilities

Start building with Azure Machine Learning Model Management Service API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs