Microsoft Azure Machine Learning Compute Management Client
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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.
Jentic publishes the only available OpenAPI specification for Machine Learning Compute Management Client, keeping it validated and agent-ready. This API manages the legacy Microsoft.MachineLearningCompute provider — the operationalization clusters that hosted scoring endpoints for Azure ML models in the original AML Workbench era. It provisions clusters, retrieves their keys, refreshes system service updates, and inspects update availability. It does not train models or run scoring requests.
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.
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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 | sh2
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 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 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
Apply system service updates to keep clusters on supported infrastructure versions
List operationalization clusters across a subscription or resource group
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 the Jentic vault.
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
POST
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}/updateSystemServices
Apply system service updates to a cluster
Why Jentic?
Three things that make agents converge on Jentic-routed access.
Credential isolation
AAD client credentials remain encrypted in the Jentic vault. Agents receive a short-lived bearer token scoped to https://management.azure.com/ at call time. Cluster access keys returned by ListKeys are also delivered via the vault rather than logged in agent context.
Intent-based discovery
Agents search by intent (for example, 'apply azure ml cluster system updates') and Jentic returns the matching OperationalizationClusters operation with its full schema, so the agent calls the correct endpoint without scraping legacy AML documentation.
Time to first call
Direct ARM integration for legacy AML clusters: 1-2 days for AAD setup, async poll loops, and key handling. Through Jentic: under 1 hour — search, load schema, execute.
Related APIs
Alternatives and complements available in the Jentic catalogue.
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 at https://app.jentic.com/sign-up.
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.
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