Azure Machine Learning Model Management Service

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

★ Only Publicly Available OpenAPI DocumentAI/MLMl Inferenceoauth223 EndpointsREST

For Agents

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.

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 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

Profile a deployed image to recommend CPU, memory, and concurrency settings

GET STARTED

Start building with Azure Machine Learning Model Management Service API

Explore with Jentic

View OpenAPI Document

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

Jentic publishes the only available OpenAPI specification for Azure Machine Learning Model Management Service, keeping it validated and agent-ready. This API manages the registered models, images, profiles, and deployed inference services on an Azure Machine Learning workspace. Operators can register a model, build a containerised image, profile its resource requirements, deploy it as a real-time service, and retrieve scoring keys or short-lived bearer tokens for callers. The API exposes 23 endpoints under the /modelmanagement/v1.0 prefix and authenticates with Azure AD OAuth 2.0.

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 though Jentic?

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

Credential isolation

Azure AD service principal credentials, scoring keys, and service tokens are stored encrypted in the Jentic vault. Agents receive short-lived Bearer tokens scoped to the AML control plane and rotation-aware scoring tokens for inference calls — raw secrets never enter the agent context.

Intent-based discovery

Agents search by intent (e.g., 'deploy an Azure ML model' or 'rotate scoring keys') and Jentic returns the matching AML model management operation with its full parameter schema.

Time to first call

Direct integration: 2-4 days for Azure AD setup, async polling, image build wiring, and key rotation. Through Jentic: under 2 hours — search, load schema, execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Complementary

Azure Machine Learning Workspaces

Provisions the workspace and managed identity that this API operates inside.

Use first to create the workspace; then use this API to register and deploy models inside it.

Complementary

Azure Machine Learning Datastore Management Client

Manages the datastores that supply training data and inference inputs for these models.

Use to register data sources before models trained on that data are registered here.

Complementary

Azure Machine Learning Compute Management Client

Provisions the AKS or ACI compute targets that deployed services run on.

Use to attach the compute target before deploying a service from this API.

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 at https://app.jentic.com/sign-up.

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}.