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Auto Publish – main to live - 2025-07-18 22:05 UTC
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articles/machine-learning/how-to-access-data-batch-endpoints-jobs.md

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@@ -59,7 +59,7 @@ Use the Azure Machine Learning SDK for Python to sign in:
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config(DefaultAzureCredential())
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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```
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If your configuration runs outside an Azure Machine Learning compute instance, you need to specify the workspace where the endpoint is deployed:

articles/machine-learning/how-to-datastore.md

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@@ -36,8 +36,9 @@ In this article, you learn how to connect to Azure data storage services with Az
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```python
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from azure.ai.ml.entities import AzureBlobDatastore
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config()
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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store = AzureBlobDatastore(
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name="",
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from azure.ai.ml.entities import AzureBlobDatastore
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from azure.ai.ml.entities import AccountKeyConfiguration
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config()
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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store = AzureBlobDatastore(
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name="blob_protocol_example",
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from azure.ai.ml.entities import AzureBlobDatastore
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from azure.ai.ml.entities import SasTokenConfiguration
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config()
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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store = AzureBlobDatastore(
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name="blob_sas_example",
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```python
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from azure.ai.ml.entities import AzureDataLakeGen2Datastore
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config()
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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store = AzureDataLakeGen2Datastore(
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name="",
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from azure.ai.ml.entities._datastore.credentials import ServicePrincipalCredentials
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config()
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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store = AzureDataLakeGen2Datastore(
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name="adls_gen2_example",
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from azure.ai.ml.entities import AzureFileDatastore
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from azure.ai.ml.entities import AccountKeyConfiguration
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config()
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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store = AzureFileDatastore(
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name="file_example",
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from azure.ai.ml.entities import AzureFileDatastore
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from azure.ai.ml.entities import SasTokenConfiguration
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config()
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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store = AzureFileDatastore(
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name="file_sas_example",
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```python
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from azure.ai.ml.entities import AzureDataLakeGen1Datastore
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config()
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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store = AzureDataLakeGen1Datastore(
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name="",
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from azure.ai.ml.entities import AzureDataLakeGen1Datastore
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from azure.ai.ml.entities._datastore.credentials import ServicePrincipalCredentials
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config()
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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store = AzureDataLakeGen1Datastore(
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name="adls_gen1_example",
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```python
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from azure.ai.ml.entities import OneLakeDatastore, OneLakeArtifact
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config()
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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store = OneLakeDatastore(
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# [Python SDK: Service principal](#tab/sdk-onelake-sp)
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```python
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from azure.ai.ml.entities import AzureDataLakeGen1Datastore
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from azure.ai.ml.entities import OneLakeDatastore, OneLakeArtifact
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from azure.ai.ml.entities._datastore.credentials import ServicePrincipalCredentials
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient.from_config()
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rom azure.ai.ml.entities import OneLakeDatastore, OneLakeArtifact
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from azure.ai.ml import MLClient
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ml_client = MLClient.from_config()
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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store = OneLakeDatastore(
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articles/machine-learning/how-to-package-models-app-service.md

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@@ -87,7 +87,7 @@ Follow these steps to prepare your system.
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1. If you're running in an Azure Machine Learning compute instance, create an `MLClient` as follows:
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ml_client = MLClient.from_config(DefaultAzureCredential())
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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```
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Otherwise, configure workspace details and get a handle to the workspace:

articles/machine-learning/how-to-package-models-moe.md

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```python
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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```
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Otherwise, configure your workspace details and get a handle to the workspace:

articles/machine-learning/how-to-package-models.md

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2. If you're running in a compute instance in Azure Machine Learning, create an `MLClient` as follows:
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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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```
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Otherwise, configure your workspace details and get a handle to the workspace:

articles/machine-learning/migrate-to-v2-command-job.md

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ml_client = MLClient.from_config(credential=DefaultAzureCredential())
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# set up pytorch environment
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env = Environment(

articles/machine-learning/migrate-to-v2-local-runs.md

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env = Environment(

articles/search/search-blob-indexer-role-based-access.md

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RBAC scope is set at the container level and flows to all blobs (documents) through permission inheritance. RBAC scope is captured during indexing as permission metadata, You can use the push APIs to upload and index content and permission metadata manually see [Indexing Permissions using the push REST API](search-index-access-control-lists-and-rbac-push-api.md), or you can use an indexer to automate data ingestion. This article focuses on the indexer approach.
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At query time, user or group identities are included in the request header via the `x-ms-query-source-authorization` parameter. The identity must match the permission metadata on documents if the user is to see the search results.
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At query time, the identity of the caller is included in the request header via the `x-ms-query-source-authorization` parameter. The identity must match the permission metadata on documents if the user is to see the search results.
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The indexer approach is built on this foundation:
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## Prerequisites
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+ [Microsoft Entra ID authentication and authorization](/entra/identity/authentication/overview-authentication). Services, apps, and users must be in the same tenant. Role assignments are used for each authenticated connection.
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+ [Microsoft Entra ID authentication and authorization](/entra/identity/authentication/overview-authentication). Services and apps must be in the same tenant. Users can be in different tenants as long as all of the tenants are Microsoft Entra ID. Role assignments are used for each authenticated connection.
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+ Azure AI Search, any region, but you must have a billable tier (basic and higher) see [Service limits](search-limits-quotas-capacity.md) for managed identity support. The search service must be [configured for role-based access](search-security-enable-roles.md) and it must [have a managed identity (either system or user)](search-howto-managed-identities-data-sources.md).
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## Limitations
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+ Permission inheritance isn't available if the blob indexer is using a [one-to-many parsing mode](/rest/api/searchservice/indexers/create?view=rest-searchservice-2025-05-01-preview&preserve-view=true#blobindexerparsingmode), such as: `delimitedText`, `jsonArray`, `jsonLines`, and `markdown` with sub-mode `oneToMany`. You must use the default parsing mode that creates one search document for each blob in the container.
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## Configure Blob storage
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Verify your blob container uses role-based access.

articles/search/search-indexer-access-control-lists-and-role-based-access.md

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## Prerequisites
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+ Microsoft Entra ID authentication and authorization. Services and apps must be in the same tenant. Role assignments are used for each authenticated connection.
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+ [Microsoft Entra ID authentication and authorization](/entra/identity/authentication/overview-authentication). Services and apps must be in the same tenant. Users can be in different tenants as long as all of the tenants are Microsoft Entra ID. Role assignments are used for each authenticated connection.
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+ Azure AI Search, any region, but you must have a billable tier (basic and higher) for managed identity support. The search service must be [configured for role-based access](search-security-enable-roles.md) and it must [have a managed identity (either system or user)](search-howto-managed-identities-data-sources.md).
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+ The `owning users`, `owning groups` and `Other` [ACL identities categories](/azure/storage/blobs/data-lake-storage-access-control#users-and-identities) are not supported during public preview. Use `named users` and `named groups` assignments instead.
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+ The following indexer features don't support permission preservation capabilities but are otherwise operational for ADLS Gen2 content-only indexing:
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+ The following indexer features don't support permission inheritance in indexed documents originating from ADLS Gen2. If you're using any of these features in a skillset or indexer, document-level permissions won't be present in the indexed content:
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+ [Custom Web API skill](cognitive-search-custom-skill-web-api.md)
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+ [GenAI Prompt skill](cognitive-search-skill-genai-prompt.md)
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+ [Knowledge store](knowledge-store-concept-intro.md)
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+ [Indexer enrichment cache](search-howto-incremental-index.md)
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+ [Debug sessions](cognitive-search-debug-session.md)
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+ One-to-many [parsing modes](/rest/api/searchservice/indexers/create?view=rest-searchservice-2025-05-01-preview&preserve-view=true#blobindexerparsingmode), such as: `delimitedText`, `jsonArray`, `jaonLines`, and `markdown` with sub-mode `oneToMany`
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## Support for the permission model
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