add demand forecast notebook links

Author: EricWrightAtWork

articles/machine-learning/how-to-auto-train-forecast.md

@@ -1028,7 +1028,7 @@ az ml job download --name $run_id --download-path . --output-name rolling_fcst_r
 
 Then, you can find the metrics results in `./named-outputs/metrics_results/evaluationResult/metrics.json` and the forecasts, in JSON lines format, in `./named-outputs/rolling_fcst_result/inference_output_file`. 
 
-For more details on rolling evaluation, see our [forecasting model evaluation article](concept-automl-forecasting-evaluation.md).   
+For more details on rolling evaluation, see our [forecasting model evaluation article](concept-automl-forecasting-evaluation.md).
 
 ## Forecasting at scale: many models
 
@@ -1300,7 +1300,9 @@ az ml job create --file automl-mm-forecasting-pipeline.yml -w <Workspace> -g <Re
 
 ---
 
-After the job finishes, the evaluation metrics can be downloaded locally using the same procedure as in the [single training run pipeline](#orchestrating-training-inference-and-evaluation-with-components-and-pipelines).
+After the job finishes, the evaluation metrics can be downloaded locally using the same procedure as in the [single training run pipeline](#orchestrating-training-inference-and-evaluation-with-components-and-pipelines). 
+
+Also see the [demand forecasting with many models notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/pipelines/1k_demand_forecasting_with_pipeline_components/automl-forecasting-demand-many-models-in-pipeline/automl-forecasting-demand-many-models-in-pipeline.ipynb) for a more detailed example.
 
 > [!NOTE]
 > The many models training and inference components conditionally partition your data according to the `partition_column_names` setting so that each partition is in its own file. This process can be very slow or fail when data is very large. In this case, we recommend partitioning your data manually before running many models training or inference.  
@@ -1355,7 +1357,6 @@ hierarchy_training_level: "store"
 
 In subsequent examples, we assume that the configuration is stored at the path, `./automl_settings_hts.yml`. 
 
-
 ### HTS pipeline
 
 Next, we define a factory function that creates pipelines for orchestration of HTS training, inference, and metric computation. The parameters of this factory function are detailed in the following table:
@@ -1570,7 +1571,9 @@ az ml job create --file automl-hts-forecasting-pipeline.yml -w <Workspace> -g <R
 
 ---
 
-After the job finishes, the evaluation metrics can be downloaded locally using the same procedure as in the [single training run pipeline](#orchestrating-training-inference-and-evaluation-with-components-and-pipelines).
+After the job finishes, the evaluation metrics can be downloaded locally using the same procedure as in the [single training run pipeline](#orchestrating-training-inference-and-evaluation-with-components-and-pipelines). 
+
+Also see the [demand forecasting with hierarchical time series notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/pipelines/1k_demand_forecasting_with_pipeline_components/automl-forecasting-demand-hierarchical-timeseries-in-pipeline/automl-forecasting-demand-hierarchical-timeseries-in-pipeline.ipynb) for a more detailed example.
 
 > [!NOTE]
 > The HTS training and inference components conditionally partition your data according to the `hierarchy_column_names` setting so that each partition is in its own file. This process can be very slow or fail when data is very large. In this case, we recommend partitioning your data manually before running HTS training or inference. 
@@ -1584,6 +1587,7 @@ After the job finishes, the evaluation metrics can be downloaded locally using t
 
 See the [forecasting sample notebooks](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/automl-standalone-jobs) for detailed code examples of advanced forecasting configuration including:
 
+* [Demand forecasting pipeline examples](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/pipelines/1k_demand_forecasting_with_pipeline_components)
 * [Deep learning models](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb)
 * [Holiday detection and featurization](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-bike-share/auto-ml-forecasting-bike-share.ipynb)
 * [Manual configuration for lags and rolling window aggregation features](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-energy-demand/automl-forecasting-task-energy-demand-advanced.ipynb)