Merge pull request #1680 from fbsolo-ms1/update-tutorial-develop-feature-set-with-custom-source

Freshness update for tutorial-develop-feature-set-with-custom-source.md . . .

Author: prmerger-automator[bot]

articles/machine-learning/tutorial-develop-feature-set-with-custom-source.md

@@ -9,7 +9,7 @@ ms.subservice: core
 ms.topic: tutorial
 author: fbsolo-ms1
 ms.author: franksolomon
-ms.date: 11/28/2023
+ms.date: 11/21/2024
 ms.reviewer: yogipandey
 ms.custom:
   - sdkv2
@@ -20,7 +20,7 @@ ms.custom:
 
 # Tutorial 5: Develop a feature set with a custom source
 
-An Azure Machine Learning managed feature store lets you discover, create, and operationalize features. Features serve as the connective tissue in the machine learning lifecycle, starting from the prototyping phase, where you experiment with various features. That lifecycle continues to the operationalization phase, where you deploy your models, and inference steps look up the feature data. For more information about feature stores, see [feature store concepts](./concept-what-is-managed-feature-store.md).
+An Azure Machine Learning managed feature store lets you discover, create, and operationalize features. Features serve as the connective tissue in the machine learning lifecycle, starting from the prototyping phase, where you experiment with various features. That lifecycle continues to the operationalization phase, where you deploy your models, and inference steps look up the feature data. For more information about feature stores, visit the [feature store concepts](./concept-what-is-managed-feature-store.md) resource.
 
 Part 1 of this tutorial series showed how to create a feature set specification with custom transformations, enable materialization and perform a backfill. Part 2 showed how to experiment with features in the experimentation and training flows. Part 3 explained recurrent materialization for the `transactions` feature set, and showed how to run a batch inference pipeline on the registered model. Part 4 described how to run batch inference.
 
@@ -36,27 +36,27 @@ In this tutorial, you'll
 > [!NOTE]
 > This tutorial uses an Azure Machine Learning notebook with **Serverless Spark Compute**.
 
-* Make sure you complete the previous tutorials in this series. This tutorial reuses feature store and other resources created in those earlier tutorials.
+* Be sure to complete the previous tutorials in this series. This tutorial reuses the feature store and other resources created in those earlier tutorials.
 
 ## Set up
 
-This tutorial uses the Python feature store core SDK (`azureml-featurestore`). The Python SDK is used for create, read, update, and delete (CRUD) operations, on feature stores, feature sets, and feature store entities.
+This tutorial uses the Python feature store core SDK (`azureml-featurestore`). The Python SDK is used for create, read, update, and delete (CRUD) operations on feature stores, feature sets, and feature store entities.
 
 You don't need to explicitly install these resources for this tutorial, because in the set-up instructions shown here, the `conda.yml` file covers them.
 
 ### Configure the Azure Machine Learning Spark notebook
 
-You can create a new notebook and execute the instructions in this tutorial step by step. You can also open and run the existing notebook *featurestore_sample/notebooks/sdk_only/5.Develop-feature-set-custom-source.ipynb*. Keep this tutorial open and refer to it for documentation links and more explanation.
+You can create a new notebook and execute the instructions in this tutorial, step by step. You can also open and run the existing notebook *featurestore_sample/notebooks/sdk_only/5.Develop-feature-set-custom-source.ipynb*. Keep this tutorial open and refer to it for documentation links and more explanation.
 
 1. On the top menu, in the **Compute** dropdown list, select **Serverless Spark Compute** under **Azure Machine Learning Serverless Spark**.
 
-2. Configure the session:
+1. Configure the session:
 
-    1. Select **Configure session** in the top status bar.
-    2. Select the **Python packages** tab, s
-    3. Select **Upload Conda file**.
-    4. Upload the *conda.yml* file that you [uploaded in the first tutorial](./tutorial-get-started-with-feature-store.md#prepare-the-notebook-environment).
-    5. Optionally, increase the session time-out (idle time) to avoid frequent prerequisite reruns.
+    1. Select **Configure session** in the top status bar
+    1. Select the **Python packages** tab, select **Upload Conda file**
+    1. 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
 
 ## Set up the root directory for the samples
 This code cell sets up the root directory for the samples. It needs about 10 minutes to install all dependencies and start the Spark session.
@@ -118,14 +118,14 @@ Next, define a feature window, and display the feature values in this feature wi
 [!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_only/5.Develop-feature-set-custom-source.ipynb?name=display-features)]
 
 ### Export as a feature set specification
-To register the feature set specification with the feature store, first save that specification in a specific format. Review the generated `transactions_custom_source` feature set specification. Open this file from the file tree to see the specification: `featurestore/featuresets/transactions_custom_source/spec/FeaturesetSpec.yaml`.
+To register the feature set specification with the feature store, first save that specification in a specific format. Review the generated `transactions_custom_source` feature set specification. Open this file from the file tree to view the specification: `featurestore/featuresets/transactions_custom_source/spec/FeaturesetSpec.yaml`.
 
 The specification has these elements:
 
 - `features`: A list of features and their datatypes.
 - `index_columns`: The join keys required to access values from the feature set.
 
-To learn more about the specification, see [Understanding top-level entities in managed feature store](./concept-top-level-entities-in-managed-feature-store.md) and [CLI (v2) feature set YAML schema](./reference-yaml-feature-set.md).
+For more information about the specification, visit the [Understanding top-level entities in managed feature store](./concept-top-level-entities-in-managed-feature-store.md) and [CLI (v2) feature set YAML schema](./reference-yaml-feature-set.md) resources.
 
 Feature set specification persistence offers another benefit: the feature set specification can be source controlled.