fix(batch-pipelines): pin mlflow version for azureml-mlflow compatibi…#3801
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…lity Update conda environments in both batch pipeline examples to fix UnsupportedModelRegistryStoreURIException caused by azureml-mlflow not registering the azureml:// URI scheme plugin on Python 3.8. Changes: - Upgrade Python from 3.8.5 (EOL) to 3.10 for Ubuntu 22.04 compat - Unpin mlflow to let azureml-mlflow pull compatible version - Fix jobtools -> joblib typo (prepare.py imports joblib) - Remove outdated pins on cloudpickle, dask, and pandas - Keep scikit-learn==1.1.2 pinned for ColumnTransformer pickle compat Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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vizhur
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Mar 27, 2026
| - jobtools | ||
| - cloudpickle==1.6.0 | ||
| - dask==2.30.0 | ||
| - scikit-learn==1.1.2 |
vizhur
reviewed
Mar 27, 2026
| - datasets | ||
| - jobtools | ||
| - cloudpickle==1.6.0 | ||
| - dask==2.30.0 |
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…lity
Pin mlflow==2.8.1 to ensure the azureml-mlflow plugin correctly registers the azureml:// URI scheme on Python 3.8.5. The unpinned mlflow resolved to an incompatible version, causing UnsupportedModelRegistryStoreURIException in prepare.py.
Also fix jobtools -> joblib typo in both conda.yml files.
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