Merge pull request #280526 from Albertyang0/2024_07-Monthly-broken-links-fix-erwright

2024_07 - Fix monthly broken links - erwright

Author: prmerger-automator[bot]

articles/machine-learning/concept-automl-forecasting-methods.md

@@ -165,7 +165,7 @@ Each Series in Own Group (1:1) | All Series in Single Group (N:1)
 -------------------| -----------------
 Naive, Seasonal Naive, Average, Seasonal Average, Exponential Smoothing, ARIMA, ARIMAX, Prophet | Linear SGD, LARS LASSO, Elastic Net, K Nearest Neighbors, Decision Tree, Random Forest, Extremely Randomized Trees, Gradient Boosted Trees, LightGBM, XGBoost, TCNForecaster
 
-More general model groupings are possible via AutoML's Many-Models solution; see our [Many Models- Automated ML 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).
+More general model groupings are possible via AutoML's Many-Models solution; see our [Many Models- Automated ML notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/pipelines/1k_demand_forecast_pipeline/aml-demand-forecast-mm-pipeline/aml-demand-forecast-mm-pipeline.ipynb).
 
 ## Next steps
 

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

@@ -1303,7 +1303,7 @@ 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). 
 
-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.
+Also see the [demand forecasting with many models notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/pipelines/1k_demand_forecast_pipeline/aml-demand-forecast-mm-pipeline/aml-demand-forecast-mm-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.  
@@ -1578,7 +1578,7 @@ 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). 
 
-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-hts.ipynb) for a more detailed example.
+Also see the [demand forecasting with hierarchical time series notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/pipelines/1k_demand_forecast_pipeline/aml-demand-forecast-hts-pipeline/aml-demand-forecast-hts.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. 
@@ -1592,7 +1592,7 @@ Also see the [demand forecasting with hierarchical time series notebook](https:/
 
 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)
+* [Demand forecasting pipeline examples](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/pipelines/1k_demand_forecast_pipeline)
 * [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)