fix: missing inference packages and mlflow version pin for tutorial CI - #3928
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Following Jayesh's approval.
This was referenced Jun 8, 2026
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The curated sklearn-1.5 environment now ships MLflow 3.x, whose mlflow.sklearn.log_model() calls the /api/2.0/mlflow/logged-models endpoint that AzureML's MLflow tracking server does not yet support (returns 404). Mirror the earlier pipeline.ipynb fix (PR #3928) by prefixing the command with 'pip install mlflow<3 -q && ...' so the training script picks up an MLflow 2.x client before calling log_model.
Chakradhar886
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Jun 8, 2026
The curated sklearn-1.5 environment now ships MLflow 3.x, whose mlflow.sklearn.log_model() calls the /api/2.0/mlflow/logged-models endpoint that AzureML's MLflow tracking server does not yet support (returns 404). Mirror the earlier pipeline.ipynb fix (PR #3928) by prefixing the command with 'pip install mlflow<3 -q && ...' so the training script picks up an MLflow 2.x client before calling log_model.
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Description
Fix CI failures in get-started-notebooks deploy and pipeline tutorials
What was failing
deploy-model.ipynb: Endpoint containers were crashing on boot with missing module errors (azureml.ai.monitoring, then azureml.contrib) because the model's conda environment didn't include packages now required by the updated inference base image.
pipeline.ipynb: Training step was failing due to a breaking change in MLflow 2.19.
What was fixed
Added azureml-ai-monitoring and azureml-contrib-services to the model's conda.yaml and requirements.txt.
Pinned mlflow<2.19 in the pipeline component command in pipeline.ipynb
Checklist