link changes

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-run-results) section.
+> Check how to navigate to the run results from the  [View run results](how-to-understand-automated-ml.md#view-job-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-runs-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-jobs-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 job is automatically triggered at the end of your experiment. This test job is only job on the best model that was recommended by automated ML. Learn how to get the [results of the remote test job](#view-remote-test-run-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 job is automatically triggered at the end of your experiment. This test job is only job on the best model that was recommended by automated ML. Learn how to get the [results of the remote test job](#view-remote-test-job-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 job, the **Details** page displays a success message. Select the **Test results** tab to see the progress of the job.
 
-1. To view the results of the test job, open the **Details** page and follow the steps in the [view results of the remote test job](#view-remote-test-run-results-preview) section. 
+1. To view the results of the test job, open the **Details** page and follow the steps in the [view results of the remote test job](#view-remote-test-job-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-run-results) section.
+> Check how to navigate to the job results from the  [View run results](../how-to-understand-automated-ml.md#view-job-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

@@ -456,9 +456,9 @@ RunDetails(run).show()
 
 Passing the `test_data` or `test_size` parameters into the `AutoMLConfig`, automatically triggers a remote test run that uses the provided test data to evaluate the best model that automated ML recommends upon completion of the experiment. This remote test run is done at the end of the experiment, once the best model is determined. See how to [pass test data into your `AutoMLConfig`](../how-to-configure-cross-validation-data-splits.md#provide-test-data-preview). 
 
-### Get test run results 
+### Get test job 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-run-results-preview) or with the following code. 
+You can get the predictions and metrics from the remote test job 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. 
 
 
 ```python
@@ -483,11 +483,11 @@ predictions_df = pd.read_csv("predictions.csv")
 
 ```
 
-The model test run generates the predictions.csv file that's stored in the default datastore created with the workspace. This datastore is visible to all users with the same subscription. Test runs are not recommended for scenarios if any of the information used for or created by the test run needs to remain private.
+The model test job generates the predictions.csv file that's stored in the default datastore created with the workspace. This datastore is visible to all users with the same subscription. Test jobs are not recommended for scenarios if any of the information used for or created by the test job needs to remain private.
 
 ### Test existing automated ML model
 
-To test other existing automated ML models created, best run or child run, use [`ModelProxy()`](/python/api/azureml-train-automl-client/azureml.train.automl.model_proxy.modelproxy) to test a model after the main AutoML run has completed. `ModelProxy()` already returns the predictions and metrics and does not require further processing to retrieve the outputs.
+To test other existing automated ML models created, best job or child job, use [`ModelProxy()`](/python/api/azureml-train-automl-client/azureml.train.automl.model_proxy.modelproxy) to test a model after the main AutoML run has completed. `ModelProxy()` already returns the predictions and metrics and does not require further processing to retrieve the outputs.
 
 > [!NOTE]
 > ModelProxy is an [experimental](/python/api/overview/azure/ml/#stable-vs-experimental) preview class, and may change at any time.