Clarify the R approach (training only)

Eugene Fedorenko (eedorenko) · Feb 20, 2020 · Feb 19, 2020 · Feb 19, 2020 · Feb 19, 2020 · Feb 19, 2020
commit b18bcfe6aaa5712667e97e62b7288a0c7a686930
diff --git a/docs/getting_started.md b/docs/getting_started.md
@@ -15,7 +15,7 @@ If you already have an Azure DevOps organization, create a [new project](https:/
 
 This repository contains a template and demonstrates how it is applied for a sample ML project ***Diabetes*** that creates a linear regression model to predict the diabetes.
 
-If the desire is to adopt this template for your project and to use it with your machine learning code, it is recommended to go through this guide as it is first and then follow the [bootstrap instructions](../bootstrap/README.md) to convert the ***Diabetes*** sample into your project starting point.
+If the desire is to adopt this template for your project and to use it with your machine learning code, it is recommended to go through this guide as it is first to make sure everything is working on your environment, and then follow the [bootstrap instructions](../bootstrap/README.md) to convert the ***Diabetes*** sample into your project starting point.
 
 
 ## Create an ARM Service Connection to deploy resources
@@ -191,7 +191,7 @@ with R on Azure ML Compute. You will also need to uncomment (i.e. include) the
 to train a model with R on Databricks. You will need
 to manually create a Databricks cluster and attach it to the ML Workspace as a
 compute (Values DB_CLUSTER_ID and DATABRICKS_COMPUTE_NAME variables should be
-specified).
+specified). The examples of ML pipelines with R have a single step training a model, they don't demonstrate how to evaluate and register a model. The evaluation and registering techniques are shown only in the Python implementation. 
 
 * The second stage of the pipeline, **Train model**, triggers the run of the ML Training Pipeline. The training pipeline will train, evaluate, and register a new model. The actual computation is performed in an [Azure Machine Learning Compute cluster](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute). In Azure DevOps, this stage runs an agentless job that waits for the completion of the Azure ML job, allowing the pipeline to wait for training completion for hours or even days without using agent resources.