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Author: Court72

articles/machine-learning/concept-model-monitoring.md

@@ -49,9 +49,10 @@ Azure Machine Learning model monitoring (preview) supports the following list of
 | Data drift | Data drift tracks changes in the distribution of a model's input data by comparing it to the model's training data or recent past production data. | Jensen-Shannon Distance, Population Stability Index, Normalized Wasserstein Distance, Two-Sample Kolmogorov-Smirnov Test, Pearson's Chi-Squared Test | Classification (tabular data), Regression (tabular data) | Production data - model inputs | Recent past production data or training data |
 | Prediction drift | Prediction drift tracks changes in the distribution of a model's prediction outputs by comparing it to validation or test labeled data or recent past production data. | Jensen-Shannon Distance, Population Stability Index, Normalized Wasserstein Distance, Chebyshev Distance, Two-Sample Kolmogorov-Smirnov Test, Pearson's Chi-Squared Test | Classification (tabular data), Regression (tabular data) | Production data - model outputs | Recent past production data or validation data |
 | Data quality | Data quality tracks the data integrity of a model's input by comparing it to the model's training data or recent past production data. The data quality checks include checking for null values, type mismatch, or out-of-bounds of values. | Null value rate, data type error rate, out-of-bounds rate | Classification (tabular data), Regression (tabular data) | production data - model inputs | Recent past production data or training data |
-| Feature attribution drift | Feature attribution drift tracks the importance or contributions of features to prediction outputs in production by comparing it to feature importance at training time | Normalized discounted cumulative gain | Classification (tabular data), Regression (tabular data) | Production data - model inputs & outputs (*see Note below*) | Training data (required) |
+| Feature attribution drift | Feature attribution drift tracks the importance or contributions of features to prediction outputs in production by comparing it to feature importance at training time | Normalized discounted cumulative gain | Classification (tabular data), Regression (tabular data) | Production data - model inputs & outputs (*see the following note*) | Training data (required) |
 
-* Note: For 'feature attribution drift' signal (during Preview), the user must create a custom data asset of type 'uri_folder' that contains joined inputs and outputs (Model Data Collector can be leveraged). Additionally, 'target_column_name' is also a required field, which specifies the prediction column in your training dataset. 
+> [!NOTE]
+> For 'feature attribution drift' signal (during Preview), the user must create a custom data asset of type 'uri_folder' that contains joined inputs and outputs (Model Data Collector can be leveraged). Additionally, 'target_column_name' is also a required field, which specifies the prediction column in your training dataset. 
   
 ## How model monitoring works in Azure Machine Learning
 

articles/machine-learning/how-to-monitor-model-performance.md

@@ -476,14 +476,12 @@ created_monitor = poller.result()
 1. In the "Add Signal" screen, select the **Feature Attribution Drift** panel.
 1. Enter a name for Feature Attribution Drift signal. Feature attribution drift currently requires a few additional steps:
 1. Configure your data assets for Feature Attribution Drift
+   1. In your model creation wizard, add your custom data asset from your [custom data collection](how-to-collect-production-data.md) called 'model inputs and outputs' which combines your joined model inputs and data assets as a separate data context. 
 
-   A.) In your model creation wizard, add your custom data asset from your [custom data collection](how-to-collect-production-data.md)called 'model inputs and outputs' which combines your joined model inputs and data assets as a separate data context. 
-      :::image type="content" source="media/how-to-monitor-models/feature-attribution-drift-inputs-outputs.png" alt-text="Screenshot showing how to configure a custom data asset with inputs and outputs joined." lightbox="media/how-to-monitor-models/feature-attribution-drift-inputs-outputs.png":::
-   
-   B.) Specify your training reference dataset that will be used in the feature attribution drift component, and select your 'target column name' field, which is required to enable feature importance. 
-
-   C.) Confirm your parameters are correct
+    :::image type="content" source="media/how-to-monitor-models/feature-attribution-drift-inputs-outputs.png" alt-text="Screenshot showing how to configure a custom data asset with inputs and outputs joined." lightbox="media/how-to-monitor-models/feature-attribution-drift-inputs-outputs.png":::
 
+   1. Specify your training reference dataset that will be used in the feature attribution drift component, and select your 'target column name' field, which is required to enable feature importance. 
+   1. Confirm your parameters are correct
 1. Adjust the data window size according to your business case.
 1. Adjust the threshold according to your need.
 1. Select **Save** to return to the "Select monitoring signals" section.