clarify new conda creation in image + conda flow

Author: saachigopal

articles/machine-learning/how-to-manage-environments-v2.md

@@ -98,7 +98,7 @@ Azure ML will start building the image from the build context when the environme
 
 You can define an environment using a standard conda YAML configuration file that includes the dependencies for the conda environment. See [Creating an environment manually](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-file-manually) for information on this standard format.
 
-You must also specify a base Docker image for this environment. Azure ML will build the conda environment on top of the Docker image provided.
+You must also specify a base Docker image for this environment. Azure ML will build the conda environment on top of the Docker image provided. If you install some Python dependencies in your Docker image, those packages will not exist in the execution environment thus causing runtime failures. By default, Azure ML will build a Conda environment with dependencies you specified, and will execute the run in that environment instead of using any Python libraries that you installed on the base image.
 
 The following example is a YAML specification file for an environment defined from a conda specification. Here the relative path to the conda file from the Azure ML environment YAML file is specified via the `conda_file` property. You can alternatively define the conda specification inline using the `conda_file` property, rather than defining it in a separate file.