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avatar Nikitha-Narendra
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ML-Monitoring

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MLmodel 457 B
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  1. flavors:
    2. 

  2.   python_function:
    3. 

  3.     env:
    4. 

  4.       conda: conda.yaml
    5. 

  5.       virtualenv: python_env.yaml
    6. 

  6.     loader_module: mlflow.sklearn
    7. 

  7.     model_path: model.pkl
    8. 

  8.     predict_fn: predict
    9. 

  9.     python_version: 3.11.5
    10. 

  10.   sklearn:
    11. 

  11.     code: null
    12. 

  12.     pickled_model: model.pkl
    13. 

  13.     serialization_format: cloudpickle
    14. 

  14.     sklearn_version: 1.3.0
    15. 

  15. mlflow_version: 2.6.0
    16. 

  16. model_uuid: e22656b5d4b84703a2611736e75ffa1a
    17. 

  17. utc_time_created: '2023-09-19 22:23:57.347143'
    18.
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Integrate Google Cloud Storage
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Use any S3 compatible storage!

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ML-Monitoring is now integrated with your S3 compatible storage!

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Integrate Azure Cloud Storage
Use Azure Storage
Select bucket
Set key
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Use Azure Cloud Storage!

Browsing data directories saved to Azure Cloud Storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Specify your Azure Storage bucket

Access key (If needed)

Congratulations!

ML-Monitoring is now integrated with Azure Cloud Storage!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes