Validation service experiment_time_out rules

Author: cartacioS

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

@@ -169,7 +169,7 @@ Some examples include:
 
 The three different `task` parameter values (the third task-type is `forecasting`, and uses a similar algorithm pool as `regression` tasks) determine the list of models to apply. Use the `whitelist` or `blacklist` parameters to further modify iterations with the available models to include or exclude. The list of supported models can be found on [SupportedModels Class](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels) for ([Classification](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.classification), [Forecasting](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting), and [Regression](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.regression)).
 
-Automated ML's validation service will require that `experiment_timeout_minutes` be set to a minimum timeout of 15 minutes in order to help avoid experiment timeout failures.
+To help avoid  experiment timeout failures, Automated ML's validation service will require that `experiment_timeout_minutes` be set to a minimum of 15 minutes, or 60 minutes if your row by column size exceeds 10 million.
 
 ### Primary Metric
 The primary metric determines the metric to be used during model training for optimization. The available metrics you can select is determined by the task type you choose, and the following table shows valid primary metrics for each task type.