Merge pull request #115661 from nibaccam/many-models

AutoML | many models accelerator links

Author: PRMerger19

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

@@ -186,8 +186,19 @@ Consider these pros and cons when choosing to use local vs. remote.
 | Register and visualize experiment's info and metrics in UI | ✓      | ✓     |                               |
 | Data guardrails                                            | ✓      | ✓     |                               |
 
+## Many models 
 
-## Automated ML in Azure Machine Learning
+The [Many Models Solution Accelerator](https://aka.ms/many-models) (preview) builds on Azure Machine Learning and enables you to use automated ML to train, operate, and manage hundreds or even thousands of machine learning models.
+
+For example, building a model __for each instance or individual__ in the following scenarios can lead to improved results:
+
+* Predicting sales for each individual store
+* Predictive maintenance for hundreds of oil wells
+* Tailoring an experience for individual users.
+
+For more information, see the [Many Models Solution Accelerator](https://aka.ms/many-models) on GitHub.
+
+## AutoML in Azure Machine Learning
 
 Azure Machine Learning offers two experiences for working with automated ML
 

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

@@ -131,7 +131,7 @@ Use custom validation dataset if random split is not acceptable, usually time se
 
 Next determine where the model will be trained. An automated machine learning training experiment can run on the following compute options:
 * Your local machine such as a local desktop or laptop – Generally when you have small dataset and you are still in the exploration stage.
-* A remote machine in the cloud – [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.
+* A remote machine in the cloud – [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. 
 
   See this [GitHub site](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning) for examples of notebooks with local and remote compute targets.
 
@@ -527,6 +527,7 @@ For general information on how model explanations and feature importance can be
 
 ## Next steps
 
-Learn more about [how and where to deploy a model](how-to-deploy-and-where.md).
++ Learn more about [how and where to deploy a model](how-to-deploy-and-where.md).
 
-Learn more about [how to train a regression model with Automated machine learning](tutorial-auto-train-models.md) or [how to train using Automated machine learning on a remote resource](how-to-auto-train-remote.md).
++ Learn more about [how to train a regression model with Automated machine learning](tutorial-auto-train-models.md) or [how to train using Automated machine learning on a remote resource](how-to-auto-train-remote.md).
++ Learn how to train multiple models with autoML in the [Many Models Solution Accelerator](https://aka.ms/many-models).