Fix EOL Python 3.8 base image in nyc_taxi_data_regression env_train#3866
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Chakradhar886 wants to merge 13 commits intomainfrom
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Fix EOL Python 3.8 base image in nyc_taxi_data_regression env_train#3866Chakradhar886 wants to merge 13 commits intomainfrom
Chakradhar886 wants to merge 13 commits intomainfrom
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Update Dockerfile from python:3.8.13 to python:3.10 and bump all dependency version ranges to be compatible with modern Python. The old python:3.8.13 image and pinned dependencies caused MLflow inference container crashes (HTTP 502 liveness probe failure) when deploying the trained model to managed online endpoints.
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Update Dockerfile from python:3.8.13 to python:3.10 and bump all dependency version ranges to be compatible with modern Python. The old python:3.8.13 image and pinned dependencies caused MLflow inference container crashes (HTTP 502 liveness probe failure) when deploying the trained model to managed online endpoints.
…azureml-examples into fix/nyc-taxi-eol-python38
…azureml-examples into fix/nyc-taxi-eol-python38
…azureml-examples into fix/nyc-taxi-eol-python38
…azureml-examples into fix/nyc-taxi-eol-python38
…azureml-examples into fix/nyc-taxi-eol-python38
…azureml-examples into fix/nyc-taxi-eol-python38
…azureml-examples into fix/nyc-taxi-eol-python38
…ployment The no-code MLflow auto-inference container was crashing with HTTP 502 liveness probe failure. Use the SKLearnEnv (env_from_registry) already created earlier in the notebook as the explicit deployment environment instead of relying on auto-inference.
…ployment The no-code MLflow auto-inference container was crashing with HTTP 502 liveness probe failure. Use the SKLearnEnv (env_from_registry) already created earlier in the notebook as the explicit deployment environment instead of relying on auto-inference.
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Update Dockerfile from python:3.8.13 to python:3.10 and bump all dependency version ranges to be compatible with modern Python.
The old python:3.8.13 image and pinned dependencies caused MLflow inference container crashes (HTTP 502 liveness probe failure) when deploying the trained model to managed online endpoints.
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