Fix: pin mlflow<2.19 to avoid logged-models API 404 error#3891
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Chakradhar886 wants to merge 7 commits intomainfrom
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Fix: pin mlflow<2.19 to avoid logged-models API 404 error#3891Chakradhar886 wants to merge 7 commits intomainfrom
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…void image build failure The sklearn-1.5 curated environment updated conda which deprecated conda info --root. This broke the custom conda_env-based Docker image build during deployment. Replacing conda_env with pip_requirements keeps the same pinned package versions while using pip-based deployment that avoids the broken conda Dockerfile template.
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The conda_env parameter in log_model() creates a conda.yaml in the model artifact, which triggers AzureML to build a custom Docker image during deployment. The new conda version in sklearn-1.5/labels/latest does not support 'conda info --root' (deprecated in favor of --base), causing the Docker build to fail. Using pip_requirements=False suppresses environment file creation entirely, so AzureML uses its pre-built MLflow inference image instead of building a custom one.
- Pin mlflow==2.17.0 in training command (sklearn-1.5 env now ships mlflow 3.x which hits unsupported API) - Use pip_requirements=False in log_model to avoid conda Docker build failure (conda info --root deprecated)
…ild failure Two issues caused by sklearn-1.5/labels/latest env update (~April 4): 1. MLflow 3.x calls unsupported API -> pin mlflow==2.17.0 in command 2. New conda deprecates 'conda info --root' -> remove conda.yaml from model artifact so AzureML uses pre-built inference image instead of building a custom Docker image
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