Microsoft Azure Machine Learning Compute Management Client

Name: Machine Learning Compute Management Client API
Brand: Machine Learning Compute Management Client
Availability: InStock

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★ Only Publicly Available OpenAPI DocumentAI/MLMl Inferenceoauth210 Endpoints

Install Jentic One Beta

Connect the Machine Learning Compute Management Client to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Machine Learning Compute 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.

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 Machine Learning Compute Management Client API.

Provision and delete operationalization clusters that host Azure ML scoring endpoints

Retrieve cluster access keys for downstream deployment tooling

Check whether system service updates are available for an existing cluster

GET STARTED

Start building with Machine Learning Compute Management Client API

Explore with Jentic

View OpenAPI Document

For Agents

Provision and operate legacy Azure ML operationalization clusters used to host model scoring endpoints. Use for cluster lifecycle and system service updates, not for training models or scoring inference requests.

Use for: Provision an operationalization cluster for Azure ML model scoring, Get the access keys for the 'aml-scoring-prod' cluster, Check whether system service updates are available for the scoring cluster, List all Microsoft.MachineLearningCompute clusters in my subscription

Not supported: Does not train models, run scoring requests, or manage modern Azure ML workspaces - use only for legacy Microsoft.MachineLearningCompute operationalization cluster lifecycle and system service updates.

Use Cases

Patterns agents use Machine Learning Compute Management Client API for, with concrete tasks.

★ Legacy AML Scoring Cluster Operations

Teams still running Azure ML Workbench-era operationalization clusters need to keep them patched and reachable. The CheckSystemServicesUpdatesAvailable and UpdateSystemServices operations let operators detect when an underlying Kubernetes or Azure Container Service version has shifted and apply the upgrade through Azure Resource Manager. This avoids a full cluster rebuild for security and version-currency updates.

Call CheckSystemServicesUpdatesAvailable on the 'aml-scoring-prod' cluster, and if updatesAvailable is true, call UpdateSystemServices and poll until the operation completes.

Cluster Inventory and Key Rotation

Security teams auditing Azure ML scoring infrastructure list every operationalization cluster across the subscription, then call ListKeys per cluster to inventory which downstream tooling holds active credentials. Combined with periodic key regeneration, this supports compliance reviews of model-serving credentials.

List all OperationalizationClusters in the subscription, then for each cluster call ListKeys and produce a CSV of cluster name, resource group, and storage account key fingerprint.

Cluster Decommission Workflow

Organisations migrating from Azure ML Workbench scoring to Azure Machine Learning workspaces need to decommission the legacy operationalization clusters in a controlled order. Listing clusters, validating no active deployments, then calling Delete drains the resource cleanly. The DELETE on /operationalizationClusters/{clusterName} is asynchronous and returns 202 Accepted with an Azure-AsyncOperation URL to poll.

List operationalization clusters tagged decom=true, then DELETE each cluster and poll its Azure-AsyncOperation URL until the operation reports Succeeded.

AI Agent Cluster Maintenance

Platform agents running scheduled maintenance use Jentic to find the right operationalization-cluster operation without bundling the legacy AML SDK. The agent searches an intent like 'check azure ml cluster system updates', loads the operation schema, and executes the call against management.azure.com with a token retrieved from your Jentic One instance.

Use Jentic to search 'check azure ml operationalization cluster system updates', load the schema, and execute against the target cluster, returning the updatesAvailable flag and the recommended update list.

Key Endpoints

10 endpoints — jentic publishes the only available openapi specification for machine learning compute management client, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

PUT

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}

Create or update an operationalization cluster

GET

/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningCompute/operationalizationClusters

List operationalization clusters in a subscription

DELETE

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}

Delete an operationalization cluster

POST

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}/checkSystemServicesUpdatesAvailable

Check whether system service updates are available

POST

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}/updateSystemServices

Apply system service updates to a cluster

PUT

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}

Create or update an operationalization cluster

GET

/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningCompute/operationalizationClusters

List operationalization clusters in a subscription

DELETE

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}

Delete an operationalization cluster

POST

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}/checkSystemServicesUpdatesAvailable

Check whether system service updates are available

Why Jentic?

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

Credential isolation

Your Azure AD credential for the MachineLearningCompute control plane is stored once, encrypted, by your own Jentic One instance and injected at execution time. It never enters the agent's prompt, logs, or context.

Intent-based discovery

Agents search Jentic by intent such as 'list ML operationalization clusters' or 'check for cluster system-service updates', and Jentic returns the matching operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

Time to first call

FAQs

Specific to using Machine Learning Compute Management Client API through Jentic.

Why is there no official OpenAPI spec for Machine Learning Compute Management Client?

Microsoft Azure does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Machine Learning Compute Management Client 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 the Machine Learning Compute Management Client use?

AAD OAuth 2.0 bearer tokens scoped to https://management.azure.com/. With Jentic, the AAD client credentials are stored encrypted in the vault and a short-lived bearer token is injected at call time.

Can I score a model with this API?

No. Scoring is a data-plane call against the cluster's exposed scoring endpoint, not against management.azure.com. This client only provisions, lists, updates, and deletes the cluster resource and exposes its access keys.

What are the rate limits for the Machine Learning Compute Management Client?

Standard Azure Resource Manager throttling: typically 12,000 reads and 1,200 writes per hour per subscription per region. Cluster create, update, and delete operations are asynchronous and return 202 Accepted with an Azure-AsyncOperation URL to poll for completion.

How do I check for cluster system updates through Jentic?

Run pip install jentic, then search Jentic for 'check azure ml operationalization cluster system updates'. Jentic returns the OperationalizationClusters_CheckSystemServicesUpdatesAvailable operation; load it, supply the cluster resource ID, and execute the POST call.

Should I use this for new Azure ML projects?

No - this is the legacy AML Workbench operationalization plane. New Azure Machine Learning workloads should use the Microsoft.MachineLearningServices provider (workspaces, online endpoints, batch endpoints). Use this API only when maintaining existing operationalization clusters.

Can I limit what my agent is allowed to do with the Machine Learning Compute Management Client?

Yes. Because this API carries the operationalization cluster name in the URL path (/operationalizationClusters/{clusterName}), your self-hosted Jentic One instance lets you write rules that pin the agent to a single cluster and to specific operations, such as listing clusters and checking whether system-service updates are available. You decide which operations the agent may call, so create, delete, and update-system-services calls stay off limits unless you explicitly add them. The Azure AD credential you store stays with your own Jentic One instance and is injected only when an allowed operation runs.

Microsoft Azure Machine Learning Compute Management Client

Connect the Machine Learning Compute Management Client to your agent

Capabilities

Start building with Machine Learning Compute Management Client API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs