Change SSL to TLS.

Author: TimShererWithAquent

articles/machine-learning/azure-machine-learning-release-notes.md

@@ -920,7 +920,7 @@ At the time of this release, the following browsers are supported: Chrome, Firef
     + Updated interface to create a `RawDataContext` to only require the data and the `AutoMLBaseSettings` object.
     +  Allow AutoML users to drop training series that are not long enough when forecasting. - Allow AutoML users to drop grains from the test set that does not exist in the training set when forecasting.
   + **azure-cli-ml**
-    + You can now update the SSL certificate for the scoring endpoint deployed on AKS cluster both for Microsoft generated and customer certificate.
+    + You can now update the TLS/SSL certificate for the scoring endpoint deployed on AKS cluster both for Microsoft generated and customer certificate.
   + **azureml-automl-core**
     + Fixed an issue in AutoML where rows with missing labels were not removed properly.
     + Improved error logging in AutoML; full error messages will now always be written to the log file.
@@ -951,7 +951,7 @@ At the time of this release, the following browsers are supported: Chrome, Firef
     + Supported importing HTTP csv/tsv files in dataset python SDK.
     + Deprecated the Workspace.setup() method. Warning message shown to users suggests using create() or get()/from_config() instead.
     + Added Environment.add_private_pip_wheel(), which enables uploading private custom python packages `whl`to the workspace and securely using them to build/materialize the environment.
-    + You can now update the SSL certificate for the scoring endpoint deployed on AKS cluster both for Microsoft generated and customer certificate.
+    + You can now update the TLS/SSL certificate for the scoring endpoint deployed on AKS cluster both for Microsoft generated and customer certificate.
   + **azureml-explain-model**
     + Added parameter to add a model ID to explanations on upload.
     + Added `is_raw` tagging to explanations in memory and upload.

articles/machine-learning/resource-known-issues.md

@@ -277,7 +277,7 @@ az aks get-credentials -g <rg> -n <aks cluster name>
 
 Updates to Azure Machine Learning components installed in an Azure Kubernetes Service cluster must be manually applied. 
 
-You can apply these updates by detaching the cluster from the Azure Machine Learning workspace, and then reattaching the cluster to the workspace. If SSL is enabled in the cluster, you will need to supply the SSL certificate and private key when reattaching the cluster. 
+You can apply these updates by detaching the cluster from the Azure Machine Learning workspace, and then reattaching the cluster to the workspace. If TLS is enabled in the cluster, you will need to supply the TLS/SSL certificate and private key when reattaching the cluster. 
 
 ```python
 compute_target = ComputeTarget(workspace=ws, name=clusterWorkspaceName)
@@ -298,7 +298,7 @@ compute_target = ComputeTarget.attach(workspace=ws, name=args.clusterWorkspaceNa
 compute_target.wait_for_completion(show_output=True)
 ```
 
-If you no longer have the SSL certificate and private key, or you are using a certificate generated by Azure Machine Learning, you can retrieve the files prior to detaching the cluster by connecting to the cluster using `kubectl` and retrieving the secret `azuremlfessl`.
+If you no longer have the TLS/SSL certificate and private key, or you are using a certificate generated by Azure Machine Learning, you can retrieve the files prior to detaching the cluster by connecting to the cluster using `kubectl` and retrieving the secret `azuremlfessl`.
 
 ```bash
 kubectl get secret/azuremlfessl -o yaml