new notebook names

Author: sdgilley

articles/machine-learning/feature-retrieval-concepts.md

@@ -69,7 +69,7 @@ featurestore1.generate_feature_retrieval_spec("./feature_retrieval_spec_folder",
 
 ```
 
-Find detailed examples in the **2. Experiment and train models using features.ipynb** notebook, hosted at [this resource](https://github.com/Azure/azureml-examples/tree/main/sdk/python/featurestore_sample/notebooks/sdk_only).
+Find detailed examples in the **2.Experiment-train-models-using-features.ipynb** notebook, hosted at [this resource](https://github.com/Azure/azureml-examples/tree/main/sdk/python/featurestore_sample/notebooks/sdk_only).
 
 The function generates a YAML file artifact, which has a structure similar to the structure in this example:
 ```yaml
@@ -149,7 +149,7 @@ import shutil
 shutil.copy(os.path.join(args.training_data, "feature_retrieval_spec.yaml"), args.model_output)
 ```
 
-Review the **2. Experiment and train models using features.ipynb** notebook, hosted at [this resource](https://github.com/Azure/azureml-examples/tree/main/sdk/python/featurestore_sample/notebooks/sdk_only), for a complete pipeline example that uses a built-in feature retrieval component to generate training data and run the training job with the packaging.
+Review the **2.Experiment-train-models-using-features.ipynb** notebook, hosted at [this resource](https://github.com/Azure/azureml-examples/tree/main/sdk/python/featurestore_sample/notebooks/sdk_only), for a complete pipeline example that uses a built-in feature retrieval component to generate training data and run the training job with the packaging.
 
 For training data generated by other methods, the feature retrieval specification can be passed as an input to the training job, and then handle the copy and package process in the training script.
 
@@ -172,7 +172,7 @@ def init()
   init_online_lookup(features, credential)
 ```
 
-Visit the **4. Enable online store and run online inference.ipynb** notebook, hosted at [this resource](https://github.com/Azure/azureml-examples/tree/main/sdk/python/featurestore_sample/notebooks/sdk_only), for a detailed code snippet.
+Visit the **4.Enable-online-store-run-inference.ipynb** notebook, hosted at [this resource](https://github.com/Azure/azureml-examples/tree/main/sdk/python/featurestore_sample/notebooks/sdk_only), for a detailed code snippet.
 
 ## Use feature retrieval specification in batch inference
 
@@ -183,7 +183,7 @@ Batch inference requires:
 
 The feature retrieval specification used in step 1 operates the same way as it does to [generate training data](#use-feature-retrieval-specification-to-create-training-data). The built-in feature retrieval component generates the inference data. As long as the feature retrieval specification is packaged with the model, the model can serve, as a convenience, as the input to the component. This approach is an alternative to directly passing the inference data in the feature retrieval specification.
 
-Visit the **3. Enable recurrent materialization and run batch inference.ipynb** notebook, hosted at [this resource](https://github.com/Azure/azureml-examples/tree/main/sdk/python/featurestore_sample/notebooks/sdk_only), for a detailed code snippet.
+Visit the **3.Enable-recurrent-materialization-run-batch-inference.ipynb** notebook, hosted at [this resource](https://github.com/Azure/azureml-examples/tree/main/sdk/python/featurestore_sample/notebooks/sdk_only), for a detailed code snippet.
 
 ## Built-in feature retrieval component
 
@@ -220,8 +220,8 @@ To use the component, reference its component ID in a pipeline job YAML file, or
 
 Review these notebooks for examples of the built-in component, both hosted at [this resource](https://github.com/Azure/azureml-examples/tree/main/sdk/python/featurestore_sample/notebooks/sdk_only):
 
- - **2. Experiment and train models using features.ipynb**
- - **3. Enable recurrent materialization and run batch inference.ipynb**
+ - **2.Experiment-train-models-using-features.ipynb**
+ - **3.Enable-recurrent-materialization-run-batch-inference.ipynb**
 
 ## Next steps
 

articles/machine-learning/feature-set-materialization-concepts.md

@@ -53,7 +53,7 @@ To avoid the limit, users should run backfill jobs in advance to [fill the gaps]
 
 Before you run a data materialization job, enable the offline and/or online data materializations at the feature set level.
 
-[!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/featurestore_sample/notebooks/sdk_only/4. Enable online store and run online inference.ipynb?name=enable-accounts-material)]
+[!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_only/4.Enable-online-store-run-inference.ipynb?name=enable-accounts-material)]
 
 You can submit the data materialization jobs as a:
 
@@ -75,7 +75,7 @@ User can submit a backfill request with:
 - A list of data materialization status values - Incomplete, Complete, or None
 - A feature window (optional)
 
-[!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/featurestore_sample/notebooks/sdk_only/1. Develop a feature set and register with managed feature store.ipynb?name=backfill-txns-fset)]
+[!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_only/1.Develop-feature-set-and-register.ipynb?name=backfill-txns-fset)]
 
 After submission of the backfill request, a new materialization job is created for each *data interval* that has a matching data materialization status (Incomplete, Complete, or None). Additionally, the relevant data intervals must fall within the defined *feature window*. If the data materialization status is `Pending` for a *data interval*, no materialization job is submitted for that interval.
 

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

@@ -46,7 +46,7 @@ You don't need to explicitly install these resources for this tutorial, because
 
 ### 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 a feature set with 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**.
 
@@ -61,17 +61,17 @@ You can create a new notebook and execute the instructions in this tutorial step
 ## 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.
 
-[!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/featurestore_sample/notebooks/sdk_only/5. Develop a feature set with custom source.ipynb?name=root-dir)]
+[!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_only/5.Develop-feature-set-custom-source.ipynb?name=root-dir)]
 
 ## Initialize the CRUD client of the feature store workspace
 Initialize the `MLClient` for the feature store workspace, to cover the create, read, update, and delete (CRUD) operations on the feature store workspace.
 
-[!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/featurestore_sample/notebooks/sdk_only/5. Develop a feature set with custom source.ipynb?name=init-fset-crud-client)]
+[!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_only/5.Develop-feature-set-custom-source.ipynb?name=init-fset-crud-client)]
 
 ## Initialize the feature store core SDK client
 As mentioned earlier, this tutorial uses the Python feature store core SDK (`azureml-featurestore`). This initialized SDK client covers create, read, update, and delete (CRUD) operations on feature stores, feature sets, and feature store entities.
 
-[!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/featurestore_sample/notebooks/sdk_only/5. Develop a feature set with custom source.ipynb?name=init-fs-core-sdk)]
+[!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_only/5.Develop-feature-set-custom-source.ipynb?name=init-fs-core-sdk)]
 
 ## Custom source definition
 You can define your own source loading logic from any data storage that has a custom source definition. Implement a source processor user-defined function (UDF) class (`CustomSourceTransformer` in this tutorial) to use this feature. This class should define an `__init__(self, **kwargs)` function, and a `process(self, start_time, end_time, **kwargs)` function. The `kwargs` dictionary is supplied as a part of the feature set specification definition. This definition is then passed to the UDF. The `start_time` and `end_time` parameters are calculated and passed to the UDF function.
@@ -111,11 +111,11 @@ class CustomSourceTransformer:
 ## Create a feature set specification with a custom source, and experiment with it locally
 Now, create a feature set specification with a custom source definition, and use it in your development environment to experiment with the feature set. The tutorial notebook attached to **Serverless Spark Compute** serves as the development environment.
 
-[!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/featurestore_sample/notebooks/sdk_only/5. Develop a feature set with custom source.ipynb?name=create-fs-custom-src)]
+[!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_only/5.Develop-feature-set-custom-source.ipynb?name=create-fs-custom-src)]
 
 Next, define a feature window, and display the feature values in this feature window.
 
-[!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/featurestore_sample/notebooks/sdk_only/5. Develop a feature set with custom source.ipynb?name=display-features)]
+[!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`.
@@ -129,22 +129,22 @@ To learn more about the specification, see [Understanding top-level entities in
 
 Feature set specification persistence offers another benefit: the feature set specification can be source controlled.
 
-[!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/featurestore_sample/notebooks/sdk_only/5. Develop a feature set with custom source.ipynb?name=dump-txn-fs-spec)]
+[!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_only/5.Develop-feature-set-custom-source.ipynb?name=dump-txn-fs-spec)]
 
 ## Register the transaction feature set with the feature store
 Use this code to register a feature set asset loaded from custom source with the feature store. You can then reuse that asset, and easily share it. Registration of a feature set asset offers managed capabilities, including versioning and materialization.
 
-[!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/featurestore_sample/notebooks/sdk_only/5. Develop a feature set with custom source.ipynb?name=register-txn-fset)]
+[!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_only/5.Develop-feature-set-custom-source.ipynb?name=register-txn-fset)]
 
 Obtain the registered feature set, and print related information.
 
-[!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/featurestore_sample/notebooks/sdk_only/5. Develop a feature set with custom source.ipynb?name=get-txn-fset)]
+[!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_only/5.Develop-feature-set-custom-source.ipynb?name=get-txn-fset)]
 
 ## Test feature generation from registered feature set
 Use the `to_spark_dataframe()` function of the feature set to test the feature generation from the registered feature set, and display the features.
 print-txn-fset-sample-values
 
-[!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/featurestore_sample/notebooks/sdk_only/5. Develop a feature set with custom source.ipynb?name=print-txn-fset-sample-values)]
+[!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_only/5.Develop-feature-set-custom-source.ipynb?name=print-txn-fset-sample-values)]
 
 You should be able to successfully fetch the registered feature set as a Spark dataframe, and then display it. You can now use these features for a point-in-time join with observation data, and the subsequent steps in your machine learning pipeline.
 

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

@@ -49,11 +49,11 @@ Before you proceed with this tutorial, be sure to complete the first and second
 
 2. Start the Spark session.
 
-   [!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/featurestore_sample/notebooks/sdk_only/3. Enable recurrent materialization and run batch inference.ipynb?name=start-spark-session)]
+   [!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_only/3.Enable-recurrent-materialization-run-batch-inference.ipynb?name=start-spark-session)]
 
 3. Set up the root directory for the samples.
 
-   [!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/featurestore_sample/notebooks/sdk_only/3. Enable recurrent materialization and run batch inference.ipynb?name=root-dir)]
+   [!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_only/3.Enable-recurrent-materialization-run-batch-inference.ipynb?name=root-dir)]
 
 4. Set up the CLI.
    ### [Python SDK](#tab/python)
@@ -64,33 +64,33 @@ Before you proceed with this tutorial, be sure to complete the first and second
 
    1. Install the Azure Machine Learning extension.
 
-      [!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/featurestore_sample/notebooks/sdk_and_cli/3. Enable recurrent materialization and run batch inference.ipynb?name=install-ml-ext-cli)]
+      [!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_and_cli/3.Enable-recurrent-materialization-run-batch-inference.ipynb?name=install-ml-ext-cli)]
 
    2. Authenticate.
 
-      [!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/featurestore_sample/notebooks/sdk_and_cli/3. Enable recurrent materialization and run batch inference.ipynb?name=auth-cli)]
+      [!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_and_cli/3.Enable-recurrent-materialization-run-batch-inference.ipynb?name=auth-cli)]
 
    3. Set the default subscription.
 
-      [!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/featurestore_sample/notebooks/sdk_and_cli/3. Enable recurrent materialization and run batch inference.ipynb?name=set-default-subs-cli)]
+      [!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_and_cli/3.Enable-recurrent-materialization-run-batch-inference.ipynb?name=set-default-subs-cli)]
 
    ---
 
 5. Initialize the project workspace CRUD (create, read, update, and delete) client.
 
    The tutorial notebook runs from this current workspace.
 
-   [!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/featurestore_sample/notebooks/sdk_only/3. Enable recurrent materialization and run batch inference.ipynb?name=init-ws-crud-client)]
+   [!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_only/3.Enable-recurrent-materialization-run-batch-inference.ipynb?name=init-ws-crud-client)]
 
 6. Initialize the feature store variables.
 
    Be sure to update the `featurestore_name` value, to reflect what you created in the first tutorial.
 
-   [!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/featurestore_sample/notebooks/sdk_only/3. Enable recurrent materialization and run batch inference.ipynb?name=init-fs-crud-client)]
+   [!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_only/3.Enable-recurrent-materialization-run-batch-inference.ipynb?name=init-fs-crud-client)]
 
 7. Initialize the feature store SDK client.
 
-   [!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/featurestore_sample/notebooks/sdk_only/3. Enable recurrent materialization and run batch inference.ipynb?name=init-fs-core-sdk)]
+   [!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_only/3.Enable-recurrent-materialization-run-batch-inference.ipynb?name=init-fs-core-sdk)]
 
 ## Enable recurrent materialization on the transactions feature set
 
@@ -109,15 +109,15 @@ To handle inference of the model in production, you might want to set up recurre
 
 As explained in earlier tutorials, after data is materialized (backfill or recurrent materialization), feature retrieval uses the materialized data by default.
 
-[!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/featurestore_sample/notebooks/sdk_only/3. Enable recurrent materialization and run batch inference.ipynb?name=enable-recurrent-mat-txns-fset)]
+[!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_only/3.Enable-recurrent-materialization-run-batch-inference.ipynb?name=enable-recurrent-mat-txns-fset)]
 
 ## (Optional) Save the YAML file for the feature set asset
 
 You use the updated settings to save the YAML file.
 
 ### [Python SDK](#tab/python)
 
-[!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/featurestore_sample/notebooks/sdk_only/3. Enable recurrent materialization and run batch inference.ipynb?name=dump-txn-fset-with-mat-yaml)]
+[!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_only/3.Enable-recurrent-materialization-run-batch-inference.ipynb?name=dump-txn-fset-with-mat-yaml)]
 
 ### [Azure CLI](#tab/cli)
 
@@ -138,7 +138,7 @@ The batch inference has these steps:
    > [!NOTE]
    > You use a job for batch inference in this example. You can also use batch endpoints in Azure Machine Learning.
 
-   [!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/featurestore_sample/notebooks/sdk_only/3. Enable recurrent materialization and run batch inference.ipynb?name=run-batch-inf-pipeline)]
+   [!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_only/3.Enable-recurrent-materialization-run-batch-inference.ipynb?name=run-batch-inf-pipeline)]
 
 ### Inspect the output data for batch inference
 
@@ -151,7 +151,7 @@ In the pipeline view:
 
    In the batch inference pipeline (*/project/fraud_mode/pipelines/batch_inference_pipeline.yaml*) outputs, because you didn't provide `name` or `version` values for `outputs` of `inference_step`, the system created an untracked data asset with a GUID as the name value and `1` as the version value. In this cell, you derive and then display the data path from the asset.
 
-   [!notebook-python[] (~/azureml-examples-temp-fix/sdk/python/featurestore_sample/notebooks/sdk_only/3. Enable recurrent materialization and run batch inference.ipynb?name=inspect-batch-inf-output-data)]
+   [!notebook-python[] (~/azureml-examples-main/sdk/python/featurestore_sample/notebooks/sdk_only/3.Enable-recurrent-materialization-run-batch-inference.ipynb?name=inspect-batch-inf-output-data)]
 
 ## Clean up