Merge pull request #5617 from MicrosoftDocs/repo_sync_working_branch

Confirm merge from repo_sync_working_branch to main to sync with https://github.com/MicrosoftDocs/azure-ai-docs (branch main)

Author: Taojunshen

articles/machine-learning/concept-automated-ml.md

@@ -171,7 +171,7 @@ Enable this setting with:
 Automated machine learning supports ensemble models, which are enabled by default. Ensemble learning improves machine learning results and predictive performance by combining multiple models as opposed to using single models. The ensemble iterations appear as the final iterations of your job. Automated machine learning uses both voting and stacking ensemble methods for combining models:
 
 * **Voting**: Predicts based on the weighted average of predicted class probabilities (for classification tasks) or predicted regression targets (for regression tasks).
-* **Stacking**: Combines heterogenous models and trains a meta-model based on the output from the individual models. The current default meta-models are LogisticRegression for classification tasks and ElasticNet for regression/forecasting tasks.
+* **Stacking**: Combines heterogeneous models and trains a meta-model based on the output from the individual models. The current default meta-models are LogisticRegression for classification tasks and ElasticNet for regression/forecasting tasks.
 
 The [Caruana ensemble selection algorithm](http://www.niculescu-mizil.org/papers/shotgun.icml04.revised.rev2.pdf) with sorted ensemble initialization is used to decide which models to use within the ensemble. At a high level, this algorithm initializes the ensemble with up to five models with the best individual scores, and verifies that these models are within 5% threshold of the best score to avoid a poor initial ensemble. Then for each ensemble iteration, a new model is added to the existing ensemble and the resulting score is calculated. If a new model improved the existing ensemble score, the ensemble is updated to include the new model.
 

articles/machine-learning/how-to-responsible-ai-insights-ui.md

@@ -111,7 +111,7 @@ Component parameters for real-life interventions use causal analysis. Do the fol
 1. **Target feature (required)**: Choose the outcome you want the causal effects to be calculated for.
 1. **Treatment features (required)**: Choose one or more features that you're interested in changing ("treating") to optimize the target outcome.
 1. **Categorical features**: Indicate which features are categorical to properly render them as categorical values in the dashboard UI. This field is pre-loaded for you based on your dataset metadata.
-1. **Advanced settings**: Specify additional parameters for your causal analysis, such as heterogenous features (that is, additional features to understand causal segmentation in your analysis, in addition to your treatment features) and which causal model you want to be used.
+1. **Advanced settings**: Specify additional parameters for your causal analysis, such as heterogeneous features (that is, additional features to understand causal segmentation in your analysis, in addition to your treatment features) and which causal model you want to be used.
 
 ## Configure your experiment
 

articles/machine-learning/how-to-use-pipeline-component.md

@@ -90,7 +90,7 @@ To access components in Azure Machine Learning studio, you need to register the
 
 You reference pipeline components as child jobs in a pipeline job just like you reference other types of components. You can provide runtime settings like `default_datastore` and `default_compute` at the pipeline job level.
 
-You need to promote any parameters you want to change during runtime as pipeline job inputs. Otherwise, they're hard-coded in the pipeline component. Promoting compute definition to a pipeline level input supports heterogenous pipelines that can use different compute targets in different steps.
+You need to promote any parameters you want to change during runtime as pipeline job inputs. Otherwise, they're hard-coded in the pipeline component. Promoting compute definition to a pipeline level input supports heterogeneous pipelines that can use different compute targets in different steps.
 
 To submit the pipeline job, edit the `cpu-cluster` in the `default_compute` section before you run the `az ml job create -f pipeline.yml` command.
 

articles/machine-learning/v1/concept-automated-ml.md

@@ -286,7 +286,7 @@ Enable this setting with:
 Automated machine learning supports ensemble models, which are enabled by default. Ensemble learning improves machine learning results and predictive performance by combining multiple models as opposed to using single models. The ensemble iterations appear as the final iterations of your job. Automated machine learning uses both voting and stacking ensemble methods for combining models:
 
 * **Voting**: predicts based on the weighted average of predicted class probabilities (for classification tasks) or predicted regression targets (for regression tasks).
-* **Stacking**: stacking combines heterogenous models and trains a meta-model based on the output from the individual models. The current default meta-models are LogisticRegression for classification tasks and ElasticNet for regression/forecasting tasks.
+* **Stacking**: stacking combines heterogeneous models and trains a meta-model based on the output from the individual models. The current default meta-models are LogisticRegression for classification tasks and ElasticNet for regression/forecasting tasks.
 
 The [Caruana ensemble selection algorithm](http://www.niculescu-mizil.org/papers/shotgun.icml04.revised.rev2.pdf) with sorted ensemble initialization is used to decide which models to use within the ensemble. At a high level, this algorithm initializes the ensemble with up to five models with the best individual scores, and verifies that these models are within 5% threshold of the best score to avoid a poor initial ensemble. Then for each ensemble iteration, a new model is added to the existing ensemble and the resulting score is calculated. If a new model improved the existing ensemble score, the ensemble is updated to include the new model.
 

articles/machine-learning/v1/how-to-create-attach-compute-cluster.md

@@ -163,7 +163,7 @@ az ml computetarget create amlcompute --name lowpriocluster --vm-size Standard_N
         compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',
                                                                 max_nodes=5,
                                                                 identity_type="UserAssigned",
-                                                                identity_id=['/subscriptions/<subcription_id>/resourcegroups/<resource_group>/providers/Microsoft.ManagedIdentity/userAssignedIdentities/<user_assigned_identity>'])
+                                                                identity_id=['/subscriptions/<subscription_id>/resourcegroups/<resource_group>/providers/Microsoft.ManagedIdentity/userAssignedIdentities/<user_assigned_identity>'])
 
         cpu_cluster_name = "cpu-cluster"
         cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)
@@ -183,7 +183,7 @@ az ml computetarget create amlcompute --name lowpriocluster --vm-size Standard_N
         ```python
         # add a user-assigned managed identity
         cpu_cluster.add_identity(identity_type="UserAssigned", 
-                                    identity_id=['/subscriptions/<subcription_id>/resourcegroups/<resource_group>/providers/Microsoft.ManagedIdentity/userAssignedIdentities/<user_assigned_identity>'])
+                                    identity_id=['/subscriptions/<subscription_id>/resourcegroups/<resource_group>/providers/Microsoft.ManagedIdentity/userAssignedIdentities/<user_assigned_identity>'])
         ```
 
 # [Azure CLI](#tab/azure-cli)
@@ -196,7 +196,7 @@ az ml computetarget create amlcompute --name lowpriocluster --vm-size Standard_N
   * User-assigned managed identity
 
     ```azurecli
-    az ml computetarget create amlcompute --name cpu-cluster --vm-size Standard_NC6 --max-nodes 5 --assign-identity '/subscriptions/<subcription_id>/resourcegroups/<resource_group>/providers/Microsoft.ManagedIdentity/userAssignedIdentities/<user_assigned_identity>'
+    az ml computetarget create amlcompute --name cpu-cluster --vm-size Standard_NC6 --max-nodes 5 --assign-identity '/subscriptions/<subscription_id>/resourcegroups/<resource_group>/providers/Microsoft.ManagedIdentity/userAssignedIdentities/<user_assigned_identity>'
     ```
 
   * System-assigned managed identity
@@ -208,7 +208,7 @@ az ml computetarget create amlcompute --name lowpriocluster --vm-size Standard_N
 
     * User-assigned managed identity
         ```azurecli
-        az ml computetarget amlcompute identity assign --name cpu-cluster '/subscriptions/<subcription_id>/resourcegroups/<resource_group>/providers/Microsoft.ManagedIdentity/userAssignedIdentities/<user_assigned_identity>'
+        az ml computetarget amlcompute identity assign --name cpu-cluster '/subscriptions/<subscription_id>/resourcegroups/<resource_group>/providers/Microsoft.ManagedIdentity/userAssignedIdentities/<user_assigned_identity>'
         ```
     * System-assigned managed identity
 

articles/machine-learning/v1/how-to-deploy-model-cognitive-search.md

@@ -27,7 +27,7 @@ monikerRange: 'azureml-api-1'
 
 This article teaches you how to use Azure Machine Learning to deploy a model for use with [Azure AI Search](/azure/search/search-what-is-azure-search).
 
-Azure AI Search performs content processing over heterogenous content, to make it queryable by humans or applications. This process can be enhanced by using a model deployed from Azure Machine Learning.
+Azure AI Search performs content processing over heterogeneous content, to make it queryable by humans or applications. This process can be enhanced by using a model deployed from Azure Machine Learning.
 
 Azure Machine Learning can deploy a trained model as a web service. The web service is then embedded in an Azure AI Search _skill_, which becomes part of the processing pipeline.
 

articles/machine-learning/v1/reference-azure-machine-learning-cli.md

@@ -148,7 +148,7 @@ The following commands demonstrate how to use the CLI to manage resources used b
   + User-assigned managed identity
 
     ```azurecli
-    az ml computetarget create amlcompute --name cpu-cluster --vm-size Standard_NC6 --max-nodes 5 --assign-identity '/subscriptions/<subcription_id>/resourcegroups/<resource_group>/providers/Microsoft.ManagedIdentity/userAssignedIdentities/<user_assigned_identity>'
+    az ml computetarget create amlcompute --name cpu-cluster --vm-size Standard_NC6 --max-nodes 5 --assign-identity '/subscriptions/<subscription_id>/resourcegroups/<resource_group>/providers/Microsoft.ManagedIdentity/userAssignedIdentities/<user_assigned_identity>'
     ```
 
   + System-assigned managed identity
@@ -160,7 +160,7 @@ The following commands demonstrate how to use the CLI to manage resources used b
 
     + User-assigned managed identity
         ```azurecli
-        az ml computetarget amlcompute identity assign --name cpu-cluster '/subscriptions/<subcription_id>/resourcegroups/<resource_group>/providers/Microsoft.ManagedIdentity/userAssignedIdentities/<user_assigned_identity>'
+        az ml computetarget amlcompute identity assign --name cpu-cluster '/subscriptions/<subscription_id>/resourcegroups/<resource_group>/providers/Microsoft.ManagedIdentity/userAssignedIdentities/<user_assigned_identity>'
         ```
     + System-assigned managed identity