Microsoft Azure Azure Machine Learning Datastore Management Client

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

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★ Only Publicly Available OpenAPI DocumentAI/MLMl Inferenceoauth28 Endpoints

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.

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 Azure Machine Learning Datastore Management Client API.

List every datastore registered to a workspace

Retrieve a single datastore by name including its credential type

GET STARTED

Start building with Azure Machine Learning Datastore Management Client API

Explore with Jentic

View OpenAPI Document

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.

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 Jentic?

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

Credential isolation

Your Azure AD service principal credentials, plus the storage SAS tokens and account keys carried in datastore bodies, 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 'register an Azure Machine Learning datastore' or 'set the workspace default datastore', and Jentic returns the matching datastore operation with its input schema, including the per-storage-type credential variants, so the agent calls the right endpoint without reading the AML docs.

Time to first call

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 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. 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.

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

Yes. Because you run Jentic One yourself, your own rules decide which datastore operations and credentials the agent may use. This API carries the subscription, resource group, workspace, and datastore name in the URL path, so you can pin the agent to the datastores on a single workspace and choose exactly which operations it may call, for example allowing it to register and list datastores while excluding the delete-datastore and set-workspace-default operations. Anything you do not add stays off limits to the agent.

Microsoft Azure Azure Machine Learning Datastore Management Client

Connect the Azure Machine Learning Datastore Management Client to your agent

Capabilities

Start building with Azure Machine Learning Datastore Management Client API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs