Azure Machine Learning Datastore Management Client

Name: Azure Machine Learning Datastore Management Client API
Brand: Azure Machine Learning Datastore Management Client
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLMl Inferenceoauth28 EndpointsREST

For Agents

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.

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 Datastore Management Client API.

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

GET STARTED

Start building with Azure Machine Learning Datastore Management Client API

Explore with Jentic

View OpenAPI Document

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

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.

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

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

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

Why though 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.

Complementary

Azure Machine Learning Workspaces

Provisions the workspace that hosts the datastores managed here.

Use to create the workspace before registering datastores against it.

Complementary

Azure Machine Learning Model Management Service

Manages models, images, and services that consume data registered via these datastores.

Use after datastores are registered to manage trained models that read from them.

Complementary

Azure Machine Learning Compute Management Client

Provisions compute targets where training jobs mount these datastores.

Use to attach compute clusters that consume data from registered datastores.

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.