Merge pull request #103136 from rastala/master

fix azureml-mlflow import

Author: PRMerger17

articles/machine-learning/how-to-use-mlflow.md

@@ -9,7 +9,7 @@ ms.service: machine-learning
 ms.subservice: core
 ms.reviewer: nibaccam
 ms.topic: conceptual
-ms.date: 01/27/2020
+ms.date: 02/03/2020
 ms.custom: seodec18
 ---
 
@@ -103,7 +103,7 @@ MLflow Tracking with Azure Machine Learning lets you store the logged metrics an
 
 Remote runs let you train your models on more powerful computes, such as GPU enabled virtual machines, or Machine Learning Compute clusters. See [Set up compute targets for model training](how-to-set-up-training-targets.md) to learn about different compute options.
 
-Configure your compute and training run environment with the [`Environment`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.environment.environment?view=azure-ml-py) class. Include `mlflow` and `azure-contrib-run` pip packages in environment's [`CondaDependencies`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.conda_dependencies.condadependencies?view=azure-ml-py) section. Then construct  [`ScriptRunConfig`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.script_run_config.scriptrunconfig?view=azure-ml-py) with your remote compute as the compute target.
+Configure your compute and training run environment with the [`Environment`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.environment.environment?view=azure-ml-py) class. Include `mlflow` and `azureml-mlflow` pip packages in environment's [`CondaDependencies`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.conda_dependencies.condadependencies?view=azure-ml-py) section. Then construct  [`ScriptRunConfig`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.script_run_config.scriptrunconfig?view=azure-ml-py) with your remote compute as the compute target.
 
 ```Python
 from azureml.core.environment import Environment
@@ -115,7 +115,7 @@ exp = Experiment(workspace = 'my_workspace',
 
 mlflow_env = Environment(name='mlflow-env')
 
-cd = CondaDependencies.create(pip_packages=['mlflow', 'azureml-contrib-run'])
+cd = CondaDependencies.create(pip_packages=['mlflow', 'azureml-mlflow'])
 
 mlflow_env.python.conda_dependencies = cd