Merge pull request #261999 from ssalgadodev/patch-51

Update how-to-troubleshoot-auto-ml.md

Author: v-ccolin

articles/machine-learning/how-to-troubleshoot-auto-ml.md

@@ -4,10 +4,11 @@ titleSuffix: Azure Machine Learning
 description: Learn how to troubleshoot and resolve issues in your automated machine learning experiments.
 author: PhaniShekhar
 ms.author: phmantri
+ms.reviewer: ssalgado
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: automl
-ms.date: 10/21/2021
+ms.date: 12/21/2023
 ms.topic: troubleshooting
 ms.custom: automl, sdkv2
 ---
@@ -20,23 +21,23 @@ In this guide, learn how to identify and resolve issues in your automated machin
 
 ## Troubleshoot automated ML for Images and NLP in Studio
 
-In case of failures in runs for Automated ML for Images and NLP, you can use the following steps to understand the error.
-1. In the studio UI, the AutoML run should have a failure message indicating the reason for failure.
-2. For more details, go to the child run of this AutoML run. This child run is a HyperDrive run.
-3. In the "Trials" tab, you can check all the trials done for this HyperDrive run.
-4. Go to the failed trial runs.
-5. These runs should have an error message in the "Status" section of the "Overview" tab indicating the reason for failure.
-   Please click on "See more details" to get more details about the failure.
-6. You can look at "std_log.txt" in the "Outputs + Logs" tab to look at detailed logs and exception traces.
-
-If your Automated ML runs uses pipeline runs for trials, you can follow the following steps to understand the error.
-1. Please follow the steps 1-4 above to identify the failed trial run.
+If there is a job failure for Automated ML for Images and NLP, you can use the following steps to understand the error.
+1. In the studio UI, the AutoML job should have a failure message indicating the reason for failure.
+2. For more details, go to the child job of this AutoML job. This child run is a HyperDrive job.
+3. In the **Trials** tab, you can check all the trials done for this HyperDrive run.
+4. Go to the failed trial job.
+5. These jobs should have an error message in the **Status** section of the **Overview** tab indicating the reason for failure.
+   Select **See more details** to get more details about the failure.
+6. Additionally you can view **std_log.txt** in the **Outputs + Logs** tab to look at detailed logs and exception traces.
+
+If your Automated ML runs uses pipeline runs for trials, follow these steps to understand the error.
+1. Follow the steps 1-4 above to identify the failed trial job.
 2. This run should show you the pipeline run and the failed nodes in the pipeline are marked with Red color.
-:::image type="content" source="./media/how-to-troubleshoot-auto-ml/pipeline-graph-sample.jpg" alt-text="Diagram that shows a failed pipeline run." lightbox="./media/how-to-troubleshoot-auto-ml/pipeline-graph-sample.jpg":::
-3. Double click the failed node in the pipeline.
-4. These runs should have an error message in the "Status" section of the "Overview" tab indicating the reason for failure.
-   Please click on "See more details" to get more details about the failure.
-5. You can look at "std_log.txt" in the "Outputs + Logs" tab to look at detailed logs and exception traces.
+:::image type="content" source="./media/how-to-troubleshoot-auto-ml/pipeline-graph-sample.jpg" alt-text="Diagram that shows a failed pipeline job." lightbox="./media/how-to-troubleshoot-auto-ml/pipeline-graph-sample.jpg":::
+3. Select the failed node in the pipeline.
+4. These jobs should have an error message in the **Status** section of the **Overview** tab indicating the reason for failure.
+   Select **See more details** to get more details about the failure.
+5. You can look at **std_log.txt** in the **Outputs + Logs** tab to look at detailed logs and exception traces.
 
 ## Next steps