final changes from Cesar

Author: sdgilley

articles/machine-learning/concept-automated-ml.md

@@ -176,20 +176,25 @@ The ONNX runtime also supports C#, so you can use the model built automatically
 
 The web interface for automated ML always uses a remote [compute target](concept-compute-target.md).  But when you use the Python SDK, you will choose either a local compute or a remote compute target for automated ML training.
 
-* **Local compute**: Training occurs on your local laptop or VM compute.
-* **Remote compute**: Training occurs on Machine Learning compute clusters.
+* **Local compute**: Training occurs on your local laptop or VM compute. 
+* **Remote compute**: Training occurs on Machine Learning compute clusters.  
 
-If your scenario is about initial explorations or demos using small data and short trains (i.e. seconds or a couple of minutes per child run), training on your local computer might be a better choice.  There is no setup time, the infrastructure resources (your PC or VM) are directly available. On a remote compute, the start up time for the internal infrastructure will add around 1.5 minutes per child run, plus additional minutes for the cluster infrastructure if the VMs are not yet up and running.
+### Choose compute target
+Consider these factors when choosing your compute target:
 
-But, if you are training with larger datasets like in production training creating models which need longer trains, remote compute will provide much better end-to-end time performance because `AutoML` will parallelize trains across the cluster's nodes.
+ * **Choose a local compute**: If your scenario is about initial explorations or demos using small data and short trains (i.e. seconds or a couple of minutes per child run), training on your local computer might be a better choice.  There is no setup time, the infrastructure resources (your PC or VM) are directly available.
+ * **Chose a remote ML compute cluster**: If you are training with larger datasets like in production training creating models which need longer trains, remote compute will provide much better end-to-end time performance because `AutoML` will parallelize trains across the cluster's nodes. On a remote compute, the start up time for the internal infrastructure will add around 1.5 minutes per child run, plus additional minutes for the cluster infrastructure if the VMs are not yet up and running.
 
+### Pros and cons
 Consider these pros and cons when choosing to use local vs. remote.
 
 |  | Pros (Advantages)  |Cons (Handicaps)  |
 |---------|---------|---------|---------|
 |**Local compute target** |  <li> No environment start up time   | <li>  Subset of features<li>  Can't parallelize runs <li> Worse for large data. <li>No data streaming while training <li>  No DNN-based featurization <li> Python SDK only |
 |**Remote ML compute clusters**|  <li> Full set of features <li> Parallelize child runs <li>   Large data support<li>  DNN-based featurization <li>  Dynamic scalability of compute cluster on demand <li> No-code experience (web UI) also available  |  <li> Start up time for cluster nodes <li> Start up time for each child run    |
 
+### Feature availability 
+
  More features are available when you use the remote compute, as shown in the table below. Some of these features are available only in an Enterprise workspace.
 
 | Feature                                                    | Remote | Local | Requires <br>Enterprise workspace |