Microsoft Azure Azure Machine Learning Model Management Service
Browse all Azure APIs50
AI ReadinessFoundational (C-)50/100
Agent-ready OpenAPI document · curated by JenticAI/MLMl Inferenceoauth223 EndpointsREST
Know of an official OpenAPI document? Contribute it →
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.
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.
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.
1
Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fazure.com%2Fazure-machine-learning-model-management-service" | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fazure.com%2Fazure-machine-learning-model-management-service" | 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 Machine Learning Model Management Service API.
Register a model artifact against a workspace and tag it for retrieval
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
Deploy a model image as a real-time scoring service on attached compute
Rotate primary or secondary scoring keys on a deployed inference service
Issue a short-lived bearer token for calling a deployed service
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
Register a model
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
Register a model
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
Jentic AI Readiness Score
This API is usable in Jentic One now. Its AI-readiness score against Jentic's framework shows where it stands today and where improvements would make it even easier for agents to use.
A
Azure Machine Learning Model Management Service
- Foundational (C-)50/100
56
Foundational Compliance53
Developer Experience & Jentic Compatibility33
AI-Readiness & Agent Experience94
Agent Usability35
Security100
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.0
Show dimension breakdown
56
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: CSignals: 4
82%
Lint Results
Aggregated quality score from linter diagnostics, weighted by severity.
100%
Resolution Completeness
Percentage of `$ref` references that resolve successfully.
0%
Specification Validity
Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).
42%
Structural Integrity
Structural correctness score based on schema issues using logarithmic dampening.
53
Developer Experience & Jentic Compatibility
Clarity, completeness, and ingestion readiness for developers and tooling.
Grade: CSignals: 4
7%
Example Density
How richly the API is illustrated with examples.
56%
Example Validity
Percentage of examples that conform to their schemas.
50%
Response Coverage
Percentage of operations with complete response definitions (success, client error, server error).
100%
Tooling Readiness
Health of API ingestion, bundling, and resolution within Jentic pipelines.
33
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: FSignals: 4
70%
Description Coverage
Coverage of descriptions across API elements.
0%
Error Standardization
Coverage of RFC 9457 Problem Details for error responses.
0%
OperationId Quality
Coverage, uniqueness, and casing consistency of operationIds for AI inference.
64%
Summary Coverage
Coverage of summaries across operations/tags/info.
94
Agent Usability
Functional utility, complexity comfort, and AI orchestration readiness.
Grade: A+Signals: 1
94%
Complexity Comfort
Agent comfort level based on API operational and structural complexity.
35
Security
Trust, risk posture, and security compliance.
Grade: FSignals: 1
35%
Authentication Strength
Average quality of security schemes based on authentication method strength (weakest link for OAuth2).
100
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: A+Signals: 1
100%
Descriptive Richness
Clarity and depth of descriptions across API elements.
Score it yourself
Every API in the directory is allowlisted, so you can re-score it with no key required.
npx @jentic/api-scorecard-cli score <openapi-url>Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring the Azure Machine Learning model management control plane by hand means registering an Azure AD app, acquiring bearer tokens for the AML resource, polling the operations endpoint through async image builds and service deploys, and managing scoring keys and short-lived service tokens yourself. Through Jentic you install once, import Azure Machine Learning Model Management Service from the API Directory, store the Azure AD service principal credentials once, and your agent calls it.
Permission scoping
This surface puts the subscription, resource group, workspace, and service id in the URL path (/modelmanagement/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/Microsoft.MachineLearningServices/workspaces/{workspace}/services/{id}), so a rule can pin your agent to models and services on one workspace. You choose the operations it may call, so ones like regenerateKeys or deleting a model are not included unless you add them.
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.
Related APIs
Alternatives and complements available in the Jentic catalogue.
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.
GET STARTED