Merge pull request #8078 from mSarts/patch-97

Update realtime-scoring-r-content.md

docs/reference-architectures/ai/realtime-scoring-r-content.md

@@ -18,8 +18,12 @@ The architecture of this workflow includes the following components.
 
 ## Considerations
 
+These considerations implement the pillars of the Azure Well-Architected Framework, which is a set of guiding tenets that can be used to improve the quality of a workload. For more information, see [Microsoft Azure Well-Architected Framework](/azure/architecture/framework).
+
 ### Performance efficiency
 
+Performance efficiency is the ability of your workload to scale to meet the demands placed on it by users in an efficient manner. For more information, see [Performance efficiency pillar overview](/azure/architecture/framework/scalability/overview).
+
 Machine learning workloads tend to be compute-intensive, both when training and when scoring new data. As a rule of thumb, try not to run more than one scoring process per core. Azure Machine Learning lets you define the number of R processes running in each container. The default is five processes. When creating a relatively simple model, such as a linear regression with a small number of variables, or a small decision tree, you can increase the number of processes. Monitor the CPU load on your cluster nodes to determine the appropriate limit on the number of containers.
 
 A GPU-enabled cluster can speed up some types of workloads, and deep learning models in particular. Not all workloads can take advantage of GPUs — only those that make heavy use of matrix algebra. For example, tree-based models, including random forests and boosting models, generally derive no advantage from GPUs.
@@ -30,6 +34,8 @@ In general, open-source R models store all their data in memory, so ensure that
 
 ### Security
 
+Security provides assurances against deliberate attacks and the abuse of your valuable data and systems. For more information, see [Overview of the security pillar](/azure/architecture/framework/security/overview).
+
 #### Network encryption
 
 If you secured the deployed web service using a TLS/SSL certificate, you can use HTTPS to connect to the service using the scoring or swagger URI. HTTPS helps secure communications between a client and a web service by encrypting communications between the two. Encryption uses Transport Layer Security (TLS). TLS is sometimes still referred to as Secure Sockets Layer (SSL), which was the predecessor of TLS.
@@ -49,6 +55,8 @@ Azure Application Insights can be used to monitor models deployed to web service
 
 ### Cost optimization
 
+Cost optimization is about looking at ways to reduce unnecessary expenses and improve operational efficiencies. For more information, see [Overview of the cost optimization pillar](/azure/architecture/framework/cost/overview).
+
 The main cost consideration in this architecture is the Kubernetes cluster's compute resources. The cluster must be large enough to handle the expected request volume at peak times, but this approach leaves resources idle at other times.
 
 ## Next steps