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Author: sdgilley

articles/machine-learning/v1/how-to-configure-auto-train-v1.md

@@ -141,7 +141,7 @@ Next determine where the model will be trained. An automated ML training experim
 
  * **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. See [this notebook](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb) for a local compute example. 
 
- * **Choose 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.[Azure Machine Learning Managed Compute](../concept-compute-target.md#amlcompute) is a managed service that enables the ability to train machine learning models on clusters of Azure virtual machines. Compute instance is also supported as a compute target.
+ * **Choose 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.[Azure Machine Learning Managed Compute](../concept-compute-target.md#azure-machine-learning-compute-managed) is a managed service that enables the ability to train machine learning models on clusters of Azure virtual machines. Compute instance is also supported as a compute target.
 
  * An **Azure Databricks cluster** in your Azure subscription. You can find more details in [Set up an Azure Databricks cluster for automated ML](../how-to-configure-databricks-automl-environment.md). See this [GitHub site](https://github.com/Azure/azureml-examples/tree/main/v1/python-sdk/tutorials/automl-with-databricks) for examples of notebooks with Azure Databricks.