Merge pull request #259922 from s-polly/stp_metadata_11-30-23

v-dirichards · web-flow · commit ee264265ab1c · 2023-12-01T10:36:26.000-06:00
[BULK] Updates to ms.subservice metadata values
diff --git a/articles/machine-learning/concept-causal-inference.md b/articles/machine-learning/concept-causal-inference.md
@@ -4,7 +4,7 @@ titleSuffix: Azure Machine Learning
 description: Make data-driven decisions and policies with the Responsible AI dashboard's integration of the causal analysis tool EconML. 
 services: machine-learning
 ms.service: machine-learning
-ms.subservice: enterprise-readiness
+ms.subservice: rai
 ms.topic:  how-to
 ms.author: mesameki
 author: mesameki
diff --git a/articles/machine-learning/concept-counterfactual-analysis.md b/articles/machine-learning/concept-counterfactual-analysis.md
@@ -4,7 +4,7 @@ titleSuffix: Azure Machine Learning
 description: Generate diverse counterfactual examples with feature perturbations to see minimal changes required to achieve desired prediction with the Responsible AI dashboard's integration of DiCE machine learning.
 services: machine-learning
 ms.service: machine-learning
-ms.subservice: enterprise-readiness
+ms.subservice: rai
 ms.topic:  how-to
 ms.author: mesameki
 author: mesameki
diff --git a/articles/machine-learning/concept-data-analysis.md b/articles/machine-learning/concept-data-analysis.md
@@ -4,7 +4,7 @@ titleSuffix: Azure Machine Learning
 description: Perform exploratory data analysis to understand feature biases and imbalances by using the Responsible AI dashboard's data analysis.
 services: machine-learning
 ms.service: machine-learning
-ms.subservice: enterprise-readiness
+ms.subservice: rai
 ms.topic:  how-to
 ms.author: mesameki
 author: mesameki
diff --git a/articles/machine-learning/concept-error-analysis.md b/articles/machine-learning/concept-error-analysis.md
@@ -4,7 +4,7 @@ titleSuffix: Azure Machine Learning
 description: Assess model error distributions in different cohorts of your dataset with the Responsible AI dashboard's integration of error analysis.
 services: machine-learning
 ms.service: machine-learning
-ms.subservice: enterprise-readiness
+ms.subservice: rai
 ms.topic:  how-to
 ms.author: mesameki
 author: mesameki
diff --git a/articles/machine-learning/concept-fairness-ml.md b/articles/machine-learning/concept-fairness-ml.md
@@ -4,7 +4,7 @@ titleSuffix: Azure Machine Learning
 description: Learn about machine learning fairness and how the Fairlearn Python package can help you assess and mitigate unfairness. 
 services: machine-learning
 ms.service: machine-learning
-ms.subservice: enterprise-readiness
+ms.subservice: rai
 ms.topic: conceptual
 ms.author: mesameki
 author: mesameki
diff --git a/articles/machine-learning/concept-foundation-models.md b/articles/machine-learning/concept-foundation-models.md
@@ -4,6 +4,7 @@ titleSuffix: Azure Machine Learning
 description: Learn about machine learning foundation models and how to use them at scale in Azure.
 services: machine-learning
 ms.service: machine-learning
+ms.subservice: training
 ms.topic: conceptual
 ms.author: swatig
 author: swatig007
diff --git a/articles/machine-learning/concept-responsible-ai-dashboard.md b/articles/machine-learning/concept-responsible-ai-dashboard.md
@@ -4,7 +4,7 @@ titleSuffix: Azure Machine Learning
 description: Learn how to use the comprehensive UI and SDK/YAML components in the Responsible AI dashboard to debug your machine learning models and make data-driven decisions.
 services: machine-learning
 ms.service: machine-learning
-ms.subservice: enterprise-readiness
+ms.subservice: rai
 ms.topic:  how-to
 ms.author: mesameki
 author: mesameki
diff --git a/articles/machine-learning/concept-responsible-ai-scorecard.md b/articles/machine-learning/concept-responsible-ai-scorecard.md
@@ -4,7 +4,7 @@ titleSuffix: Azure Machine Learning
 description: Learn about how to use the Responsible AI scorecard to share responsible AI insights from your machine learning models and make data-driven decisions with non-technical and technical stakeholders.
 services: machine-learning
 ms.service: machine-learning
-ms.subservice: enterprise-readiness
+ms.subservice: rai
 ms.topic:  conceptual
 ms.author: mesameki
 author: mesameki
diff --git a/articles/machine-learning/concept-responsible-ai.md b/articles/machine-learning/concept-responsible-ai.md
@@ -4,7 +4,7 @@ titleSuffix: Azure Machine Learning
 description: Learn what Responsible AI is and how to use it with Azure Machine Learning to understand models, protect data, and control the model lifecycle.
 services: machine-learning
 ms.service: machine-learning
-ms.subservice: enterprise-readiness
+ms.subservice: rai
 ms.topic: conceptual
 ms.author: mesameki
 author: mesameki
diff --git a/articles/machine-learning/concept-sourcing-human-data.md b/articles/machine-learning/concept-sourcing-human-data.md
@@ -4,6 +4,7 @@ description: Learn best practices for mitigating potential harm to people—espe
 author: nhu-do-1
 ms.author: nhudo
 ms.service: machine-learning
+ms.subservice: rai
 ms.reviewer: lagayhar
 ms.topic: conceptual 
 ms.date: 11/04/2022
diff --git a/articles/machine-learning/how-to-deploy-models-from-huggingface.md b/articles/machine-learning/how-to-deploy-models-from-huggingface.md
@@ -4,6 +4,7 @@ titleSuffix: Azure Machine Learning
 description: Deploy and score transformers based large language models from the Hugging Face hub. 
 services: machine-learning
 ms.service: machine-learning
+ms.subservice: training
 ms.custom: devx-track-python
 ms.topic: how-to
 ms.reviewer: ssalgado
@@ -166,7 +167,7 @@ Follow this link to find [hugging face model example code](https://github.com/Az
 HuggingFace hub has thousands of models with hundreds being updated each day. Only the most popular models in the collection are tested and others may fail with one of the below errors.
 
 ### Gated models
-[Gated models](https://huggingface.co/docs/hub/models-gated) require users to agree to share their contact information and accept the model owners’ terms and conditions in order to access the model. Attempting to deploy such models will fail with a `KeyError`.
+[Gated models](https://huggingface.co/docs/hub/models-gated) require users to agree to share their contact information and accept the model owners' terms and conditions in order to access the model. Attempting to deploy such models will fail with a `KeyError`.
 
 ### Models that need to run remote code
 Models typically use code from the transformers SDK but some models run code from the model repo. Such models need to set the parameter `trust_remote_code` to `True`. Follow this link to learn more about using [remote code](https://huggingface.co/docs/transformers/custom_models#using-a-model-with-custom-code). Such models are not supported from keeping security in mind. Attempting to deploy such models will fail with the following error: `ValueError: Loading <model> requires you to execute the configuration file in that repo on your local machine. Make sure you have read the code there to avoid malicious use, then set the option trust_remote_code=True to remove this error.`
diff --git a/articles/machine-learning/how-to-machine-learning-interpretability.md b/articles/machine-learning/how-to-machine-learning-interpretability.md
@@ -4,7 +4,7 @@ titleSuffix: Azure Machine Learning
 description: Learn how your machine learning model makes predictions during training and inferencing by using the Azure Machine Learning CLI and Python SDK.
 services: machine-learning
 ms.service: machine-learning
-ms.subservice: enterprise-readiness
+ms.subservice: rai
 ms.topic: how-to
 ms.author: mesameki
 author: mesameki
diff --git a/articles/machine-learning/how-to-responsible-ai-dashboard.md b/articles/machine-learning/how-to-responsible-ai-dashboard.md
@@ -4,7 +4,7 @@ titleSuffix: Azure Machine Learning
 description: Learn how to use the various tools and visualization charts in the Responsible AI dashboard in Azure Machine Learning.
 services: machine-learning
 ms.service: machine-learning
-ms.subservice: enterprise-readiness
+ms.subservice: rai
 ms.topic:  how-to
 ms.reviewer: lagayhar
 ms.author: mithigpe
@@ -50,7 +50,7 @@ You can also find this information on the Responsible AI dashboard page by selec
 
 ### Enable full functionality of the Responsible AI dashboard
 
-1. Select a running compute instance in the **Compute** dropdown list at the top of the dashboard. If you don’t have a running compute, create a new compute instance by selecting the plus sign (**+**) next to the dropdown. Or you can select the **Start compute** button to start a stopped compute instance. Creating or starting a compute instance might take few minutes.
+1. Select a running compute instance in the **Compute** dropdown list at the top of the dashboard. If you don't have a running compute, create a new compute instance by selecting the plus sign (**+**) next to the dropdown. Or you can select the **Start compute** button to start a stopped compute instance. Creating or starting a compute instance might take few minutes.
 
     :::image type="content" source="./media/how-to-responsible-ai-dashboard/select-compute.png" alt-text="Screenshot of the 'Compute' dropdown box for selecting a running compute instance." lightbox = "./media/how-to-responsible-ai-dashboard/select-compute.png":::
     
@@ -113,7 +113,7 @@ You can name your new dataset cohort, select **Add filter** to add each filter y
 
 :::image type="content" source="./media/how-to-responsible-ai-dashboard/view-dashboard-new-cohort.png" alt-text="Screenshot of making a new cohort in the dashboard." lightbox= "./media/how-to-responsible-ai-dashboard/view-dashboard-new-cohort.png":::
 
-Select **Dashboard configuration** to open a panel with a list of the components you’ve configured on your dashboard. You can hide components on your dashboard by selecting the **Trash** icon, as shown in the following image:
+Select **Dashboard configuration** to open a panel with a list of the components you've configured on your dashboard. You can hide components on your dashboard by selecting the **Trash** icon, as shown in the following image:
 
 :::image type="content" source="./media/how-to-responsible-ai-dashboard/dashboard-configuration.png" alt-text="Screenshot showing the dashboard configuration." lightbox="./media/how-to-responsible-ai-dashboard/dashboard-configuration.png":::
 
@@ -232,7 +232,7 @@ The **Chart view** panel shows you aggregate and individual plots of datapoints.
 
 ### Feature importances (model explanations)
 
-By using the model explanation component, you can see which features were most important in your model’s predictions. You can view what features affected your model’s prediction overall on the **Aggregate feature importance** pane or view feature importances for individual data points on the **Individual feature importance** pane.
+By using the model explanation component, you can see which features were most important in your model's predictions. You can view what features affected your model's prediction overall on the **Aggregate feature importance** pane or view feature importances for individual data points on the **Individual feature importance** pane.
 
 #### Aggregate feature importances (global explanations)
 
diff --git a/articles/machine-learning/how-to-responsible-ai-image-dashboard.md b/articles/machine-learning/how-to-responsible-ai-image-dashboard.md
@@ -4,7 +4,7 @@ titleSuffix: Azure Machine Learning
 description: Learn how to use the various tools and visualization charts in the Responsible AI image dashboard in Azure Machine Learning.
 services: machine-learning
 ms.service: machine-learning
-ms.subservice: enterprise-readiness
+ms.subservice: rai
 ms.topic:  how-to
 ms.reviewer: lagayhar
 ms.author: ilmat
@@ -109,7 +109,7 @@ For image classification and multiclassification, incorrect predictions refer to
 For object detection, incorrect predictions refer to images where:
 
 - At least one object was incorrectly labeled
-- Incorrectly detecting an object class when a ground truth object doesn’t exist
+- Incorrectly detecting an object class when a ground truth object doesn't exist
 - Failing to detect an object class when a ground truth object exists
 
 > [!NOTE]
@@ -165,7 +165,7 @@ The Class view pane breaks down your model predictions by class label. You can i
 
 - **Select label type**: Choose to view images by the predicted or ground truth label.
 - **Select labels to display**: View image instances containing your selection of one or more class labels.
-- **View images per class label**: Identify successful and error image instances per selected class label(s), and the distribution of each class label in your dataset. If a class label has “10/120 examples”, out of 120 total images in the dataset, 10 images belong to that class label.
+- **View images per class label**: Identify successful and error image instances per selected class label(s), and the distribution of each class label in your dataset. If a class label has "10/120 examples", out of 120 total images in the dataset, 10 images belong to that class label.
 
 Class view for multiclass classification:
 
@@ -180,8 +180,8 @@ Class view for object detection:
 For AutoML image classification models, four kinds of explainability methods are supported, namely [Guided backprop](https://arxiv.org/abs/1412.6806), [Guided gradCAM](https://arxiv.org/abs/1610.02391v4), [Integrated Gradients](https://arxiv.org/abs/1703.01365) and [XRAI](https://arxiv.org/abs/1906.02825). To learn more about the four explainability methods, see [Generate explanations for predictions](how-to-auto-train-image-models.md#generate-explanations-for-predictions).
 
 > [!NOTE]
-> -	**These four methods are specific to AutoML image classification only** and will not work with other task types such as object detection, instance segmentation etc. Non-AutoML image classification models can leverage SHAP vision for model interpretability. 
->-	**The explanations are only generated for the predicted class**. For multilabel classification, a threshold on confidence score is required, to select the classes for which the explanations are generated. See the [parameter list](how-to-responsible-ai-vision-insights.md#responsible-ai-vision-insights-component-parameter-automl-specific) for the parameter name.
+> -    **These four methods are specific to AutoML image classification only** and will not work with other task types such as object detection, instance segmentation etc. Non-AutoML image classification models can leverage SHAP vision for model interpretability. 
+>-    **The explanations are only generated for the predicted class**. For multilabel classification, a threshold on confidence score is required, to select the classes for which the explanations are generated. See the [parameter list](how-to-responsible-ai-vision-insights.md#responsible-ai-vision-insights-component-parameter-automl-specific) for the parameter name.
 
 Both AutoML and non-AutoML object detection models can leverage [D-RISE](https://github.com/microsoft/vision-explanation-methods) to generate visual explanations for model predictions.
 
diff --git a/articles/machine-learning/how-to-responsible-ai-insights-ui.md b/articles/machine-learning/how-to-responsible-ai-insights-ui.md
diff --git a/articles/machine-learning/how-to-responsible-ai-scorecard.md b/articles/machine-learning/how-to-responsible-ai-scorecard.md
diff --git a/articles/machine-learning/how-to-responsible-ai-text-dashboard.md b/articles/machine-learning/how-to-responsible-ai-text-dashboard.md
diff --git a/articles/machine-learning/how-to-responsible-ai-text-insights.md b/articles/machine-learning/how-to-responsible-ai-text-insights.md
diff --git a/articles/machine-learning/how-to-responsible-ai-vision-insights.md b/articles/machine-learning/how-to-responsible-ai-vision-insights.md
diff --git a/articles/machine-learning/how-to-use-foundation-models.md b/articles/machine-learning/how-to-use-foundation-models.md
diff --git a/articles/machine-learning/troubleshooting-managed-feature-store.md b/articles/machine-learning/troubleshooting-managed-feature-store.md
