Update how-to-use-automated-ml-for-ml-models.md

Added Explain best model and removed concurrency option as it is moved out of addition configuration to Limits Optional Point. Will be adding Limits Option in my next Pull Request.

Author: Rajveermathur

articles/machine-learning/how-to-use-automated-ml-for-ml-models.md

@@ -125,8 +125,8 @@ Otherwise, you see a list of your recent automated  ML experiments, including th
     Primary metric| Main metric used for scoring your model. [Learn more about model metrics](how-to-configure-auto-train.md#primary-metric).
    Debug model via the Responsible AI dashboard | Generate a Responsible AI dashboard to do a holistic assessment and debugging of the recommended best model. This includes insights such as model explanations, fairness and performance explorer, data explorer, model error analysis. [Learn more about how you can generate a Responsible AI dashboard.](./how-to-responsible-ai-insights-ui.md). RAI Dashboard can only be run if 'Serverless' compute (preview) is specified in the experiment set-up step.
     Blocked algorithm| Select algorithms you want to exclude from the training job. <br><br> Allowing algorithms is only available for [SDK experiments](how-to-configure-auto-train.md#supported-algorithms). <br> See the [supported algorithms for each task type](/python/api/azureml-automl-core/azureml.automl.core.shared.constants.supportedmodels).
-    Exit criterion| When any of these criteria are met, the training job is stopped. <br> *Training job time (hours)*: How long to allow the training job to run. <br> *Metric score threshold*:  Minimum metric score for all pipelines. This ensures that if you have a defined target metric you want to reach, you don't spend more time on the training job than necessary.
-    Concurrency| *Max concurrent iterations*: Maximum number of pipelines (iterations) to test in the training job. The job won't run more than the specified number of iterations. Learn more about how automated ML performs [multiple child jobs on clusters](how-to-configure-auto-train.md#multiple-child-runs-on-clusters).
+    Explain best model| Automatically shows explainability on the best model created by Automated ML.
+ 
 
 1. (Optional) View featurization settings: if you choose to enable **Automatic featurization** in the **Additional configuration settings** form, default featurization techniques are applied. In the **View featurization settings**, you can change these defaults and customize accordingly. Learn how to [customize featurizations](#customize-featurization).