mlflow · BenWilson2 · Sep 6, 2022 · Sep 2, 2022 · Sep 2, 2022 · Sep 2, 2022
diff --git a/dev/run-python-flavor-tests.sh b/dev/run-python-flavor-tests.sh
@@ -5,7 +5,6 @@ export MLFLOW_HOME=$(pwd)
 
 # TODO: Run tests for h2o, shap, and paddle in the cross-version-tests workflow
 pytest \
-  tests/azureml \
   tests/utils/test_model_utils.py \
   tests/h2o \
   tests/shap \

diff --git a/docs/source/cli.rst b/docs/source/cli.rst
@@ -4,7 +4,7 @@ Command-Line Interface
 ======================
 
 The MLflow command-line interface (CLI) provides a simple interface to various functionality in MLflow. You can use the CLI to run projects, start the tracking UI, create and list experiments, download run artifacts,
-serve MLflow Python Function and scikit-learn models, and serve models on
+serve MLflow Python Function and scikit-learn models, serve MLflow Python Function and scikit-learn models, and serve models on
 `Microsoft Azure Machine Learning <https://azure.microsoft.com/en-us/services/machine-learning-service/>`_
 and `Amazon SageMaker <https://aws.amazon.com/sagemaker/>`_.
 

diff --git a/docs/source/python_api/mlflow.azureml.rst b/docs/source/python_api/mlflow.azureml.rst
diff --git a/mlflow/azureml/__init__.py b/mlflow/azureml/__init__.py
diff --git a/mlflow/azureml/cli.py b/mlflow/azureml/cli.py
diff --git a/mlflow/cli.py b/mlflow/cli.py
@@ -579,14 +579,6 @@ def gc(older_than, backend_store_uri, run_ids):
 except ImportError as e:
     pass
 
-
-try:
-    import mlflow.azureml.cli  # pylint: disable=unused-import
-
-    cli.add_command(mlflow.azureml.cli.commands)
-except ImportError as e:
-    pass
-
 try:
     import mlflow.sagemaker.cli  # pylint: disable=unused-import
 

diff --git a/mlflow/deployments/__init__.py b/mlflow/deployments/__init__.py
@@ -1,8 +1,9 @@
 """
 Exposes functionality for deploying MLflow models to custom serving tools.
 
-Note: model deployment to AWS Sagemaker and AzureML can currently be performed via the
-:py:mod:`mlflow.sagemaker` and :py:mod:`mlflow.azureml` modules, respectively.
+Note: model deployment to AWS Sagemaker can currently be performed via the
+:py:mod:`mlflow.sagemaker` module. Model deployment to Azure can be performed by using the
+`azureml library <https://pypi.org/project/azureml-mlflow/>`_.
 
 MLflow does not currently provide built-in support for any other deployment targets, but support
 for custom targets can be installed via third-party plugins. See a list of known plugins

diff --git a/requirements/extra-ml-requirements.txt b/requirements/extra-ml-requirements.txt
@@ -1,8 +1,6 @@
 ## This file describes extra ML library dependencies that you, as an end user,
 ## must install in order to use various MLflow Python modules.
 ##
-# Required by mlflow.azureml
-azureml-sdk==1.2.0
 # Required by mlflow.keras
 keras
 # Required by mlflow.sklearn

diff --git a/tests/azureml/__init__.py b/tests/azureml/__init__.py