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Add fsspec auth option example #429

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20 changes: 20 additions & 0 deletions articles/machine-learning/how-to-access-data-interactive.md
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Expand Up @@ -222,6 +222,26 @@ df = pd.concat(dflist)
df.head()
```

#### Use different authentication
The Filesystem Class accepts a parameter ml_client, which can be used to use the same auth as the one for the ml_client authentication, for example the user managed identity by providing the correct client_id.
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Copilot AI Sep 24, 2025

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The sentence has grammatical issues. Consider revising to: 'The Filesystem class accepts an ml_client parameter, which allows you to use the same authentication as the ml_client, for example user managed identity by providing the correct client_id.'

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The Filesystem Class accepts a parameter ml_client, which can be used to use the same auth as the one for the ml_client authentication, for example the user managed identity by providing the correct client_id.
The Filesystem class accepts an ml_client parameter, which allows you to use the same authentication as the ml_client, for example user managed identity by providing the correct client_id.

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```python
from azureml.fsspec import AzureMachineLearningFileSystem
from azure.identity import ManagedIdentityCredential
from azure.ai.ml import MLClient

credential = ManagedIdentityCredential(client_id='<client_id>') # Replace with your client ID if needed

Comment on lines +232 to +233
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Copilot AI Sep 24, 2025

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The comment suggests client_id is optional ('if needed'), but the code shows it as required in the placeholder. Consider clarifying when client_id is actually required versus optional for managed identity scenarios.

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credential = ManagedIdentityCredential(client_id='<client_id>') # Replace with your client ID if needed
# For system-assigned managed identity, omit client_id:
credential = ManagedIdentityCredential()
# For user-assigned managed identity, provide your client_id:
# credential = ManagedIdentityCredential(client_id='<client_id>')

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ml_client = MLClient.from_config(credential=credential)

# define the URI - update <> placeholders
uri = 'azureml://subscriptions/<subid>/resourcegroups/<rgname>/workspaces/<workspace_name>/datastores/<datastore_name>'

# auth credential from ml_client will be used by filesystem
fs = AzureMachineLearningFileSystem(uri, ml_client=ml_client)

fs.ls()
```

#### Accessing data from your Azure Databricks filesystem (`dbfs`)

Filesystem spec (`fsspec`) has a range of [known implementations](https://filesystem-spec.readthedocs.io/en/stable/_modules/index.html), including the Databricks Filesystem (`dbfs`).
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