Bookmark Change

Author: ssalgadodev

articles/machine-learning/how-to-auto-train-image-models.md

@@ -522,7 +522,7 @@ When you've configured your AutoML Job to the desired settings, you can submit t
 The automated ML training runs generates output model files, evaluation metrics, logs and deployment artifacts like the scoring file and the environment file which can be viewed from the outputs and logs and metrics tab of the child runs.
 
 > [!TIP]
-> Check how to navigate to the run results from the  [View run results](how-to-understand-automated-ml.md#view-job-results) section.
+> Check how to navigate to the run results from the  [View run results](how-to-understand-automated-ml.md#view-run-results) section.
 
 For definitions and examples of the performance charts and metrics provided for each run, see [Evaluate automated machine learning experiment results](how-to-understand-automated-ml.md#metrics-for-image-models-preview)
 

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

@@ -155,7 +155,7 @@ Error message:
 
 ## Data schema
 
-When you try to create a new automated ML experiment via the **Edit and submit** button in the Azure Machine Learning studio, the data schema for the new experiment must match the schema of the data that was used in the original experiment. Otherwise, an error message similar to the following results. Learn more about how to [edit and submit experiments from the studio UI](how-to-use-automated-ml-for-ml-models.md#edit-and-submit-jobs-preview).
+When you try to create a new automated ML experiment via the **Edit and submit** button in the Azure Machine Learning studio, the data schema for the new experiment must match the schema of the data that was used in the original experiment. Otherwise, an error message similar to the following results. Learn more about how to [edit and submit experiments from the studio UI](how-to-use-automated-ml-for-ml-models.md#edit-and-submit-runs-preview).
 
 Error message non-vision experiments: ` Schema mismatch error: (an) additional column(s): "Column1: String, Column2: String, Column3: String", (a) missing column(s)`
 

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

@@ -146,7 +146,7 @@ Otherwise, you'll see a list of your recent automated  ML experiments, including
 
         1. Forecasting tasks only supports k-fold cross validation.
 
-    1. Provide a test dataset (preview) to evaluate the recommended model that automated ML generates for you at the end of your experiment. When you provide test data, a test run is automatically triggered at the end of your experiment. This test run is only run on the best model that was recommended by automated ML. Learn how to get the [results of the remote test run](#view-remote-test-job-results-preview).
+    1. Provide a test dataset (preview) to evaluate the recommended model that automated ML generates for you at the end of your experiment. When you provide test data, a test run is automatically triggered at the end of your experiment. This test run is only run on the best model that was recommended by automated ML. Learn how to get the [results of the remote test run](#view-remote-test-run-results-preview).
 
         >[!IMPORTANT]
         > Providing a test dataset to evaluate generated models is a preview feature. This capability is an [experimental](/python/api/overview/azure/ml/#stable-vs-experimental) preview feature, and may change at any time.
@@ -257,7 +257,7 @@ After your experiment completes, you can test the model(s) that automated ML gen
 1. Select the **Test** button. The schema of the test dataset should match the training dataset, but the **target column** is optional.
 1. Upon successful creation of model test run, the **Details** page displays a success message. Select the **Test results** tab to see the progress of the run.
 
-1. To view the results of the test run, open the **Details** page and follow the steps in the [view results of the remote test run](#view-remote-test-job-results-preview) section. 
+1. To view the results of the test run, open the **Details** page and follow the steps in the [view results of the remote test run](#view-remote-test-run-results-preview) section. 
 
     ![Test model form](./media/how-to-use-automated-ml-for-ml-models/test-model-form.png)
 

articles/machine-learning/v1/how-to-auto-train-image-models-v1.md

@@ -393,7 +393,7 @@ automl_image_run = experiment.submit(automl_image_config)
 The automated ML training runs generates output model files, evaluation metrics, logs and deployment artifacts like the scoring file and the environment file which can be viewed from the outputs and logs and metrics tab of the child runs.
 
 > [!TIP]
-> Check how to navigate to the run results from the  [View run results](../how-to-understand-automated-ml.md#view-job-results) section.
+> Check how to navigate to the run results from the  [View run results](../how-to-understand-automated-ml.md#view-run-results) section.
 
 For definitions and examples of the performance charts and metrics provided for each run, see [Evaluate automated machine learning experiment results](../how-to-understand-automated-ml.md#metrics-for-image-models-preview)
 

articles/machine-learning/v1/how-to-configure-auto-train-v1.md

@@ -458,7 +458,7 @@ Passing the `test_data` or `test_size` parameters into the `AutoMLConfig`, autom
 
 ### Get test run results 
 
-You can get the predictions and metrics from the remote test run from the [Azure Machine Learning studio](../how-to-use-automated-ml-for-ml-models.md#view-remote-test-job-results-preview) or with the following code. 
+You can get the predictions and metrics from the remote test run from the [Azure Machine Learning studio](../how-to-use-automated-ml-for-ml-models.md#view-remote-test-run-results-preview) or with the following code. 
 
 
 ```python