Resolve comments from Cesar

Author: sdgilley

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

@@ -142,14 +142,20 @@ See the [how-to](how-to-configure-auto-train.md#ensemble) for changing default e
 
 With Azure Machine Learning, you can use automated ML to build a Python model and have it converted to the ONNX format. The ONNX runtime supports  C#, so you can use the model built automatically in your C# apps without any need for recoding or any of the network latencies that REST endpoints introduce. Try an example of this flow [in this Jupyter notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb).
 
-## <a name="local-remote"></a>Local and remote compute targets
+## <a name="local-remote"></a>Guidance on local vs. remote compute targets
 
-With the Azure automated ML, you choose either a local computer or a remote compute target to train your models.  More features are available when you use the remote compute.  If you have small data and want to get started quickly, training on your local computer is your best choice.  When you use a remote compute, such as Azure Managed compute clusters, factor in setup time.  Setup time can be around 2 minutes per child run, plus additional minutes for the cluster infrastructure if they are not yet up and running.
+When using Azure Automated ML (with the Python SDK), you choose either a local compute or a remote compute target to train your models.
+
+More features are available when you use the remote compute, as shown in the table below. However, when you use a remote compute, such as Azure Managed compute clusters, factor in setup time. The internal infrastructure preparation time 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.
+
+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.
+
+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.
 
 | | Best for | Pros (Advantages)  |Cons (Handicaps)  |
 |---------|---------|---------|---------|
-|Local compute target | Small data, getting started, quick demos     |   No environment setup time, infrastructure resources are directly available  |  Subset of features<br/> Can't parallelize <br/>    Can't use large data <br/>  No DNN-based featurization <br/> Python SDK only |
-|Remote compute target| Larger data, production data, production training    |  Full set of features <br/> Parallelize child runs <br/>  Large data support <br/> DNN-based featurization <br/> Dynamic scalability of compute cluster on demand <br/>No-code experience also available in studio        |    Start up time for cluster nodes <br/> Start up time for each child run    |
+|Local compute target | Small data,  quick demos     |   No environment setup time, infrastructure resources (your PC or VM) are directly available  |  Subset of features<br/> Can't parallelize runs <br/>Worse for large data. No data streaming while training. <br/>  No DNN-based featurization <br/> Python SDK only |
+|Remote compute target| Larger data, production data, production training    |  Full set of features <br/> Parallelize child runs <br/>  Large data support <br/> DNN-based featurization <br/> Dynamic scalability of compute cluster on demand <br/>No-code experience (web UI) also available in Azure ML     |    Start up time for cluster nodes <br/> Start up time for each child run    |
 
 The following table shows a summary of features available on local and remote compute targets.  Some of these features are available only in an Enterprise workspace.
 
@@ -160,7 +166,6 @@ The following table shows a summary of features available on local and remote co
 | Feature engineering customization UI                       | ✓      |       |                               |
 | Prophet or ARIMA models for forecasting                    | ✓      |       | ✓                             |
 | Multiple runs/iterations in parallel                       | ✓      |       | ✓                             |
-| Cancel an iteration                                        | ✓      |       |                               |
 | Continue a run                                             | ✓      |       |                               |
 | Create and run experiments in studio web experience        | ✓      |       | ✓                             |
 | Feature Sweeping (or advanced transformers)                | ?      | ?     |                               |