update how to deploy custom docker image instructions. User cannot bring his own custom inference stack, we will add our own stack on the base image provided by the user.

Author: keerthiadu

articles/machine-learning/how-to-deploy-custom-docker-image.md

@@ -229,13 +229,12 @@ myenv.inferencing_stack_version = "latest"  # This will install the inference sp
 # Define the packages needed by the model and scripts
 from azureml.core.conda_dependencies import CondaDependencies
 conda_dep = CondaDependencies()
-# Unless you are using your own custom inference stack,
 # you must list azureml-defaults as a pip dependency
 conda_dep.add_pip_package("azureml-defaults")
 myenv.python.conda_dependencies=conda_dep
 ```
 
-Please note that unless you are also using your own custom inference stack, you must add azureml-defaults with version >= 1.0.45 as a pip dependency. This package contains the functionality needed to host the model as a web service.
+You must add azureml-defaults with version >= 1.0.45 as a pip dependency. This package contains the functionality needed to host the model as a web service. You must also set inferencing_stack_version property on the environment to "latest", this will install specific apt packages needed by web service. 
 
 After defining the environment, use it with an [InferenceConfig](https://docs.microsoft.com/python/api/azureml-core/azureml.core.model.inferenceconfig?view=azure-ml-py) object to define the inference environment in which the model and web service will run.