Update how-to-track-designer-experiments.md

Author: ssalgadodev

articles/machine-learning/how-to-track-designer-experiments.md

@@ -18,11 +18,11 @@ ms.custom: designer, sdkv1, event-tier1-build-2022
 
 In this article, you learn how to add logging code to designer pipelines. You also learn how to view those logs using the Azure Machine Learning studio web portal.
 
-For more information on logging metrics using the SDK authoring experience, see [Monitor Azure ML experiment jobs and metrics](how-to-log-view-metrics.md).
+For more information on logging metrics using the SDK authoring experience, see [Monitor Azure ML experiment runs and metrics](how-to-log-view-metrics.md).
 
 ## Enable logging with Execute Python Script
 
-Use the [Execute Python Script](./algorithm-module-reference/execute-python-script.md) component to enable logging in designer pipelines. Although you can log any value with this workflow, it's especially useful to log metrics from the __Evaluate Model__ component to track model performance across jobs.
+Use the [Execute Python Script](./algorithm-module-reference/execute-python-script.md) component to enable logging in designer pipelines. Although you can log any value with this workflow, it's especially useful to log metrics from the __Evaluate Model__ component to track model performance across runs.
 
 The following example shows you how to log the mean squared error of two trained models using the Evaluate Model and Execute Python Script components.
 
@@ -58,7 +58,7 @@ The following example shows you how to log the mean squared error of two trained
 
 This code uses the Azure Machine Learning Python SDK to log values. It uses Run.get_context() to get the context of the current job. It then logs values to that context with the job.parent.log() method. It uses `parent` to log values to the parent pipeline job rather than the component job.
 
-For more information on how to use the Python SDK to log values, see [Enable logging in Azure ML training jobs](how-to-log-view-metrics.md).
+For more information on how to use the Python SDK to log values, see [Enable logging in Azure ML training runs](how-to-log-view-metrics.md).
 
 ## View logs
 
@@ -77,5 +77,5 @@ In this article, you learned how to use logs in the designer. For next steps, se
 
 
 * Learn how to troubleshoot designer pipelines, see [Debug & troubleshoot ML pipelines](how-to-debug-pipelines.md#azure-machine-learning-designer).
-* Learn how to use the Python SDK to log metrics in the SDK authoring experience, see [Enable logging in Azure ML training jobs](how-to-log-view-metrics.md).
+* Learn how to use the Python SDK to log metrics in the SDK authoring experience, see [Enable logging in Azure ML training runs](how-to-log-view-metrics.md).
 * Learn how to use [Execute Python Script](./algorithm-module-reference/execute-python-script.md) in the designer.