Microsoft Azure Azure Machine Learning Datastore Management Client
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For Agents
Register and manage Azure Machine Learning datastores — abstractions over Blob, File, Data Lake, SQL, and PostgreSQL storage that ML jobs reference instead of raw connection strings.
Use for: I need to register a new Azure Blob datastore on my ML workspace, List every datastore on a Machine Learning workspace, Get the default datastore for a workspace, Set a datastore as the workspace default
Not supported: Does not move data, train models, or provision compute — use for registering and managing datastore references on an Azure Machine Learning workspace only.
Jentic publishes the only available OpenAPI specification for Azure Machine Learning Datastore Management Client, keeping it validated and agent-ready. This API manages datastores attached to an Azure Machine Learning workspace — registered references to Azure Blob, Azure File, Azure Data Lake, SQL, and PostgreSQL data sources that training jobs and pipelines mount instead of hard-coding storage credentials. Operators can register a datastore, list every datastore on a workspace, set or read the workspace default, and remove datastores that are no longer used.
Install Jentic One Beta
Connect the Azure Machine Learning Datastore Management Client to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Azure Machine Learning Datastore 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.
1
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 Azure Machine Learning Datastore Management Client API.
Register a new datastore against an Azure Machine Learning workspace
List every datastore registered to a workspace
Retrieve a single datastore by name including its credential type
Update an existing datastore's connection or credential properties
Set the workspace default datastore for unqualified path references
Delete a datastore that is no longer in use
Use Cases
Patterns agents use Azure Machine Learning Datastore Management Client API for, with concrete tasks.
★ Workspace Bootstrap with Standard Datastores
Data science teams bootstrap a new Azure Machine Learning workspace with a known set of datastores so notebooks and pipelines can mount training data without supplying credentials. The POST on the workspace's datastores endpoint registers a datastore with a name, type (AzureBlob, AzureDataLake, AzureFile, AzureSqlDatabase, or AzurePostgreSql), and the underlying credential reference. Idempotent re-registration keeps environments aligned across dev, staging, and prod.
Register a datastore named 'training-data' of type AzureBlob on workspace 'ml-prod' in resource group 'rg-ml', pointing at storage account 'mlstoreprod' container 'training' using SAS auth.
Default Datastore Promotion
Promoting a datastore to default lets pipelines reference paths without naming the datastore explicitly, which is useful when a team migrates from one storage account to another. The PUT on /workspaces/{workspaceName}/default/{name} updates the workspace pointer atomically; the previous default remains registered and can still be referenced by name.
Set datastore 'training-data-v2' as the default on workspace 'ml-prod' in resource group 'rg-ml' and confirm via the GET default endpoint.
Credential Rotation on Datastores
When the underlying storage account rotates a SAS token or key, the datastore record needs the new credential reference or jobs will start failing on mount. The PUT on a named datastore replaces the credential payload in place without re-registering, which preserves dependent dataset and pipeline references that hold the datastore name.
Update datastore 'training-data' on workspace 'ml-prod' to use a new SAS token retrieved from Key Vault secret 'training-store-sas'.
Agent-Driven Datastore Inventory
An AI agent through Jentic can enumerate datastores across every Machine Learning workspace, flag any that point at decommissioned storage accounts, and delete or update them in bulk. Jentic returns the datastore CRUD operations with full schemas so the agent does not need to interpret the AML control plane docs.
List datastores across every workspace in subscription '00000000-0000-0000-0000-000000000000' and delete any whose storageAccountName equals 'mlstore-deprecated'.
Key Endpoints
8 endpoints — jentic publishes the only available openapi specification for azure machine learning datastore management client, keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
POST
/datastore/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores
Register a new datastore
GET
/datastore/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores
List datastores on a workspace
GET
/datastore/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores/{name}
Get a datastore by name
PUT
/datastore/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/default/{name}
Set the workspace default datastore
GET
/datastore/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/default
Get the workspace default datastore
DELETE
/datastore/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores/{name}
Delete a datastore
POST
/datastore/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores
Register a new datastore
GET
/datastore/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores
List datastores on a workspace
GET
/datastore/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores/{name}
Get a datastore by name
PUT
/datastore/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/default/{name}
Set the workspace default datastore
GET
/datastore/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/default
Get the workspace default datastore
DELETE
/datastore/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores/{name}
Delete a datastore
Why Jentic?
Three things that make agents converge on Jentic-routed access.
Credential isolation
Storage SAS tokens, account keys, and service principal secrets used by the datastore body are stored encrypted in the Jentic vault. Azure AD tokens for the AML control plane are issued just-in-time and scoped — raw secrets never enter the agent context.
Intent-based discovery
Agents search by intent (e.g., 'register an Azure ML datastore') and Jentic returns the matching AML datastore operation with its parameter schema, including the per-storage-type credential variants.
Time to first call
Direct integration: 1 day for Azure AD setup, AML control-plane wiring, and per-storage-type credential payload handling. Through Jentic: under 1 hour — search, load schema, execute.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Azure Machine Learning Datastore Management Client API through Jentic.
Why is there no official OpenAPI spec for Azure Machine Learning Datastore Management Client?
Microsoft Azure does not publish a single consolidated OpenAPI specification for the AML datastore control plane. Jentic generates and maintains this spec so that AI agents and developers can call Azure Machine Learning Datastore 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 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. Jentic stores the service principal credentials in its vault and injects scoped tokens at call time, so client secrets never enter the agent context.
Which storage types can I register as a datastore?
The datastore body supports Azure Blob, Azure File, Azure Data Lake (Gen 1 and Gen 2), Azure SQL Database, and Azure PostgreSQL. Each type accepts its own credential block — SAS, account key, service principal, or username/password — set on POST and updatable via PUT.
How do I register a datastore through Jentic?
Search Jentic for 'register an Azure Machine Learning datastore', load the resulting POST operation on /datastore/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datastores, then execute it with the workspace name and storage configuration. Install with pip install jentic and run the async search-load-execute flow.
What is the workspace default datastore used for?
When a job or dataset references a path without naming a datastore explicitly, AML resolves it against the default. The GET on /workspaces/{workspaceName}/default returns it; the PUT on /default/{name} promotes a datastore to default.
What are the rate limits for this API?
Azure Machine Learning applies workspace-level throttling on control-plane datastore operations rather than a fixed per-endpoint rate. Datastore CRUD is light enough that quotas rarely bind in practice; the spec does not declare narrower limits.
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