incorporating feedback

Author: ShawnJackson

articles/machine-learning/tutorial-enable-materialization-backfill-data.md

@@ -59,8 +59,8 @@ You can create a new notebook and execute the instructions in this tutorial step
 
 1. Configure the session:
 
-   1. Select **Configure session**.
-   1. Select **Upload Conda file**.
+   1. On the toolbar, select **Configure session**.
+   1. On the **Python packages** tab, select **Upload Conda file**.
    1. Upload the *conda.yml* file that you [uploaded in the first tutorial](./tutorial-get-started-with-feature-store.md#prepare-the-notebook-environment).
    1. Increase the session time-out (idle time) to avoid frequent prerequisite reruns.
 
@@ -193,8 +193,8 @@ This UAI is assigned to the feature store shortly. It requires these permissions
 | Scope                                      | Role                            |
 |------------------------------------------------|--------------------------------------------|
 | Feature store                                  | Azure Machine Learning Data Scientist role |
-| Storage account of the offline store on the feature store | Blob storage Data Contributor role         |
-| Storage accounts of the source data                | Blob storage Data Reader role              |
+| Storage account of the offline store on the feature store | Storage Blob Data Contributor role         |
+| Storage accounts of the source data                | Storage Blob Data Reader role              |
 
 The next CLI commands assign the first two roles to the UAI. In this example, the "storage accounts of the source data" scope doesn't apply because you read the sample data from a public access blob storage. To use your own data sources, you must assign the required roles to the UAI. To learn more about access control, see [Manage access control for managed feature store](./how-to-setup-access-control-feature-store.md).
 
@@ -210,17 +210,17 @@ The next CLI commands assign the first two roles to the UAI. In this example, th
 
 ---
 
-### Grant the blob Data Reader role access to your user account in the offline store
+### Grant the Storage Blob Data Reader role access to your user account in the offline store
 
-If the feature data is materialized, you need the blob Data Reader role to read feature data from the offline materialization store.
+If the feature data is materialized, you need the Storage Blob Data Reader role to read feature data from the offline materialization store.
 
 Obtain your Azure AD object ID value from the Azure portal, as described in [Find the user object ID](/partner-center/find-ids-and-domain-names#find-the-user-object-id).
 
 To learn more about access control, see [Manage access control for managed feature store](./how-to-setup-access-control-feature-store.md).
 
 [!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_only/2. Enable materialization and backfill feature data.ipynb?name=grant-rbac-to-user-identity)]
 
-The following steps grant the blob Data Reader role access to your user account:
+The following steps grant the Storage Blob Data Reader role access to your user account:
 
 1. Attach the offline materialization store and UAI, to enable the offline store on the feature store.
 

articles/machine-learning/tutorial-enable-recurrent-materialization-run-batch-inference.md

@@ -44,8 +44,8 @@ Before you proceed with the following procedures, be sure to complete the first,
 
    1. Configure the session:
 
-      1. When the status bar displays **Configure session**, select it.
-      1. Select **Upload conda file**.
+      1. When the toolbar displays **Configure session**, select it.
+      1. On the **Python packages** tab, select **Upload conda file**.
       1. Upload the *conda.yml* file that you [uploaded in the first tutorial](./tutorial-get-started-with-feature-store.md#prepare-the-notebook-environment).
       1. Optionally, increase the session time-out (idle time) to avoid frequent prerequisite reruns.
 

articles/machine-learning/tutorial-experiment-train-models-using-features.md

@@ -46,8 +46,8 @@ Before you proceed with the following procedures, be sure to complete the first
 
    1. Configure the session:
 
-      1. When the status bar displays **Configure session**, select it.
-      1. Select **Upload conda file**.
+      1. When the toolbar displays **Configure session**, select it.
+      1. On the **Python packages** tab, select **Upload Conda file**.
       1. Upload the *conda.yml* file that you [uploaded in the first tutorial](./tutorial-get-started-with-feature-store.md#prepare-the-notebook-environment).
       1. Optionally, increase the session time-out (idle time) to avoid frequent prerequisite reruns.
 
@@ -178,7 +178,7 @@ In the following steps, you select a list of features, run a training pipeline,
 
    The first tutorial covered this step, when you registered the `transactions` feature set. Because you also have an `accounts` feature set, you can browse through the available features:
 
-   1. Go to the [Azure Machine Learning global landing page](https://ml.azure.com/home?flight=FeatureStores).
+   1. Go to the [Azure Machine Learning global landing page](https://ml.azure.com/home).
    1. On the left pane, select **Feature stores**.
    1. In the list of feature stores, select the feature store that you created earlier.
 
@@ -214,13 +214,13 @@ In this procedure, you manually trigger the training pipeline. In a production s
 
    The training pipeline has these steps:
 
-   1. Generate training data. For its input, this built-in component takes the feature retrieval specification, the observation data, and the time-stamp column name. It then generates the training data as output. It runs these steps as a managed Spark job.
+   1. Feature retrieval: For its input, this built-in component takes the feature retrieval specification, the observation data, and the time-stamp column name. It then generates the training data as output. It runs these steps as a managed Spark job.
 
-   1. Based on the training data, train the model and then generate a model (not yet registered).
+   1. Training: Based on the training data, this step trains the model and then generates a model (not yet registered).
 
-   1. Evaluate whether the model performance and quality fall within a threshold. (In this tutorial, it's a placeholder step for illustration purposes.)
+   1. Evaluation: This step validates whether the model performance and quality fall within a threshold. (In this tutorial, it's a placeholder step for illustration purposes.)
 
-   1. Register the model.
+   1. Register the model: This step registers the model.
 
       > [!NOTE]
       > In the second tutorial, you ran a backfill job to materialize data for the `transactions` feature set. The feature retrieval step reads feature values from the offline store for this feature set. The behavior is the same, even if you use the `get_offline_features()` API.

articles/machine-learning/tutorial-get-started-with-feature-store.md

@@ -32,7 +32,7 @@ This tutorial is the first part of a four-part series. Here, you learn how to:
 This tutorial series has two tracks:
 
 * The SDK-only track uses only Python SDKs. Choose this track for pure, Python-based development and deployment.
-* The SDK and CLI track uses the Python SDK for feature set development and testing only, and it uses the CLI for CRUD operations (create, update, and delete). This track is useful in continuous integration and continuous delivery (CI/CD) or GitOps scenarios, where CLI/YAML is preferred.
+* The SDK and CLI track uses the Python SDK for feature set development and testing only, and it uses the CLI for CRUD (create, read, update, and delete) operations. This track is useful in continuous integration and continuous delivery (CI/CD) or GitOps scenarios, where CLI/YAML is preferred.
 
 [!INCLUDE [machine-learning-preview-generic-disclaimer](includes/machine-learning-preview-generic-disclaimer.md)]
 
@@ -75,7 +75,7 @@ This tutorial uses an Azure Machine Learning Spark notebook for development.
 1. On the **Configure session** panel, select **Python packages**.
 
 1. Upload the Conda file:
-   1. Select **Upload Conda file**.
+   1. On the **Python packages** tab, select **Upload Conda file**.
    1. Browse to the directory that hosts the Conda file.
    1. Select **conda.yml**, and then select **Open**.
 
@@ -231,7 +231,7 @@ As a best practice, entities help enforce use of the same join key definition ac
 
 1. Initialize the feature store CRUD client.
 
-   As explained earlier in this tutorial, `MLClient` is used for creating, updating, and deleting a feature store asset. The notebook code cell sample shown here searches for the feature store that you created in an earlier step. Here, you can't reuse the same `ml_client` value that you used earlier in this tutorial, because it's scoped at the resource group level. Proper scoping is a prerequisite for feature store creation. 
+   As explained earlier in this tutorial, `MLClient` is used for creating, reading, updating, and deleting a feature store asset. The notebook code cell sample shown here searches for the feature store that you created in an earlier step. Here, you can't reuse the same `ml_client` value that you used earlier in this tutorial, because it's scoped at the resource group level. Proper scoping is a prerequisite for feature store creation. 
 
    In this code sample, the client is scoped at feature store level.
 
@@ -247,7 +247,7 @@ As a best practice, entities help enforce use of the same join key definition ac
 
 1. Initialize the feature store CRUD client.
 
-   As explained earlier in this tutorial, `MLClient` is used for creating, updating, and deleting a feature store asset. The notebook code cell sample shown here searches for the feature store that you created in an earlier step. Here, you can't reuse the same `ml_client` value that you used earlier in this tutorial, because it's scoped at the resource group level. Proper scoping is a prerequisite for feature store creation.
+   As explained earlier in this tutorial, `MLClient` is used for creating, reading, updating, and deleting a feature store asset. The notebook code cell sample shown here searches for the feature store that you created in an earlier step. Here, you can't reuse the same `ml_client` value that you used earlier in this tutorial, because it's scoped at the resource group level. Proper scoping is a prerequisite for feature store creation.
 
    In this code sample, the client is scoped at the feature store level, and it registers the `account` entity with the feature store. Additionally, it creates an account entity that has the join key `accountID` of type `string`.