Fixes for:

MicrosoftDocs/azure-docs-pr#50103

MicrosoftDocs/azure-docs-pr#50101

Author: j-martens

articles/machine-learning/service/concept-azure-machine-learning-architecture.md

@@ -163,7 +163,7 @@ A compute target is the compute resource used to run your training script or hos
 * Azure Container Instance
 * Azure Kubernetes Service
 
-Compute targets are attached to a workspace. Computer targets other than the local machine are shared by users of the workspace.
+Compute targets are attached to a workspace. Compute targets other than the local machine are shared by users of the workspace.
 
 Most compute targets can be created directly through the workspace by using the Azure portal, Azure Machine Learning SDK, or Azure CLI. If you have compute targets that were created by another process (for example, the Azure portal or Azure CLI), you can add (attach) them to your workspace. Some compute targets must be created outside the workspace, and then attached.
 

articles/machine-learning/service/how-to-migrate.md

@@ -76,7 +76,7 @@ In the latest version, models are deployed as web services to [Azure Container I
 Learn more in these articles:
 + [Deploy to ACI](how-to-deploy-to-aci.md)
 + [Deploy to AKS](how-to-deploy-to-aks.md)
-+ [Tutorial:Deploy models with Azure Machine Learning service](tutorial-deploy-models-with-aml.md)
++ [Tutorial: Deploy models with Azure Machine Learning service](tutorial-deploy-models-with-aml.md)
 
 When [support for the previous CLI ends](overview-what-happened-to-workbench.md#timeline), you won't be able to manage the web services you originally deployed with your Model Management account. However, those web services will continue to work for as long as Azure Container Service (ACS) is still supported.
 

articles/machine-learning/service/how-to-set-up-training-targets.md

@@ -32,7 +32,7 @@ Azure Machine Learning supports the following compute targets:
 
 __[Azure Container Instances (ACI)](#aci)__ can also be used to train models. It is a serverless cloud offering that is inexpensive and easy to create and work with. ACI does not support GPU acceleration, automated hyper parameter tuning, or automated model selection. Also, it cannot be used in a pipeline.
 
-The key differentiators between the computer targets are:
+The key differentiators between the compute targets are:
 * __GPU acceleration__: GPUs are available with the Data Science Virtual Machine and Azure Batch AI. You may have access to a GPU on your local computer, depending on the hardware, drivers, and frameworks that are installed.
 * __Automated hyperparameter tuning__: Azure Machine Learning automated hyperparameter optimization helps you find the best hyperparameters for your model.
 * __Automated model selection__: Azure Machine Learning can intelligently recommend algorithm and hyperparameter selection when building a model. Automated model selection helps you converge to a high-quality model faster than manually trying different combinations. For more information, see the [Tutorial: Automatically train a classification model with Azure Automated Machine Learning](tutorial-auto-train-models.md) document.