address review

Author: EricWrightAtWork

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

@@ -43,8 +43,6 @@ For this article you need,
 
 * The ability to launch AutoML training jobs. Follow the [how-to guide for setting up AutoML](how-to-configure-auto-train.md) for details.
 
-    [!INCLUDE [automl-sdk-version](../../includes/machine-learning-automl-sdk-version.md)]
-
 ## Training and validation data
 
 Input data for AutoML forecasting must contain valid time series in tabular format. Each variable must have its own corresponding column in the data table. AutoML requires at least two columns: a **time column** representing the time axis and the **target column** which is the quantity to forecast. Other columns can serve as predictors. For more details, see [how AutoML uses your data](./concept-automl-forecasting-methods.md#how-automl-uses-your-data). 
@@ -337,7 +335,7 @@ returned_job.services["Studio"].endpoint
 
 ## Forecasting with a trained model
 
-Once you've used to AutoML to train and select a best model, the next step is to evaluate the model. If it meets your requirements, you can use it to generate forecasts into the future. This section shows how to write Python scripts for evaluation and prediction (inference). For an example of deploying a trained model with an inference script, see our [example notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb).
+Once you've used AutoML to train and select a best model, the next step is to evaluate the model. If it meets your requirements, you can use it to generate forecasts into the future. This section shows how to write Python scripts for evaluation and prediction. For an example of deploying a trained model with an inference script, see our [example notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/automl-standalone-jobs/automl-forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb).
 
 ### Evaluating model accuracy with a rolling forecast
 
@@ -403,7 +401,7 @@ In this sample, the step size for the rolling forecast is set to one which means
 
 ### Prediction into the future
 
-The [forecast_quantiles()](/python/api/azureml-train-automl-client/azureml.train.automl.model_proxy.modelproxy#forecast-quantiles-x-values--typing-any--y-values--typing-union-typing-any--nonetype----none--forecast-destination--typing-union-typing-any--nonetype----none--ignore-data-errors--bool---false-----azureml-data-abstract-dataset-abstractdataset) function allows specifications of when predictions should start, unlike the `predict()` method, which is typically used for classification and regression tasks. The forecast_quantiles() method, by default, generates a point forecast that doesn't have a cone of uncertainty around it. Learn more in the [Forecasting away from training data notebook](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-forecast-function/auto-ml-forecasting-function.ipynb).
+The [forecast_quantiles()](python/api/azureml-training-tabular/azureml.training.tabular.models.forecasting_pipeline_wrapper_base.forecastingpipelinewrapperbase#azureml-training-tabular-models-forecasting-pipeline-wrapper-base-forecastingpipelinewrapperbase-forecast-quantiles) generates forecasts for given quantiles of the prediction distribution. This method thus provides a way to get a point forecast with a cone of uncertainty around it. Learn more in the [Forecasting away from training data notebook](https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-forecast-function/auto-ml-forecasting-function.ipynb).
 
 In the following example, you first replace all values in `y_pred` with `NaN`. The forecast origin is at the end of training data in this case. However, if you replaced only the second half of `y_pred` with `NaN`, the function would leave the numerical values in the first half unmodified, but forecast the `NaN` values in the second half. The function returns both the forecasted values and the aligned features.
 
@@ -413,20 +411,20 @@ You can also use the `forecast_destination` parameter in the `forecast_quantiles
 label_query = test_labels.copy().astype(np.float)
 label_query.fill(np.nan)
 label_fcst, data_trans = fitted_model.forecast_quantiles(
-    test_dataset, label_query, forecast_destination=pd.Timestamp(2019, 1, 8))
+    test_dataset, label_query, forecast_destination=pd.Timestamp(2019, 1, 8)
+)
 ```
 
-You may want to understand the predictions at a specific quantile of the distribution. For example, when the forecast is used to control inventory like grocery items or virtual machines for a cloud service. In such cases, the control point is usually something like "we want the item to be in stock and not run out 99% of the time". The following sample demonstrates how to specify which quantiles you want to see for your predictions, such as 50th or 95th percentile: 
+No quantiles are specified here, so only the point forecast is generated. You may want to understand the predictions at a specific quantile of the distribution. For example, when the forecast is used to control inventory like grocery items or virtual machines for a cloud service. In such cases, the control point is usually something like "we want the item to be in stock and not run out 99% of the time". The following sample demonstrates how to specify forecast quantiles, such as 50th or 95th percentile: 
 
 ```python
 # Get forecasts for the 5th, 50th, and 90th percentiles 
 fitted_model.quantiles = [0.05, 0.5, 0.9]
 fitted_model.forecast_quantiles(
-    test_dataset, label_query, forecast_destination=pd.Timestamp(2019, 1, 8))
+    test_dataset, label_query, forecast_destination=pd.Timestamp(2019, 1, 8)
+)
 ```
 
-If you don't specify any quantiles then only the median predictions are returned.
-
 You can calculate model metrics like, root mean squared error (RMSE) or mean absolute percentage error (MAPE) to help you estimate the models performance. See the Evaluate section of the [Bike share demand notebook](~/azureml-examples-main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb) for an example. 
 
 After the overall model accuracy has been determined, the most realistic next step is to use the model to forecast unknown future values. 
@@ -457,7 +455,7 @@ Grouping is a concept in time series forecasting that allows time series to be c
 
 ### Many models
 
-The Azure Machine Learning many models solution with automated machine learning allows users to train and manage millions of models in parallel. Many models The solution accelerator uses [Azure Machine Learning pipelines](concept-ml-pipelines.md) to train the model. Specifically, a [Pipeline](/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline%28class%29) object and [ParalleRunStep](/python/api/azureml-pipeline-steps/azureml.pipeline.steps.parallelrunstep) are used and require specific configuration parameters set through the [ParallelRunConfig](/python/api/azureml-pipeline-steps/azureml.pipeline.steps.parallelrunconfig). 
+The Azure Machine Learning many models solution with automated machine learning allows users to train and manage millions of models in parallel. The Many Models Solution Accelerator uses [Azure Machine Learning pipelines](concept-ml-pipelines.md) to train the model. Specifically, a [Pipeline](/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline%28class%29) object and [ParalleRunStep](/python/api/azureml-pipeline-steps/azureml.pipeline.steps.parallelrunstep) are used and require specific configuration parameters set through the [ParallelRunConfig](/python/api/azureml-pipeline-steps/azureml.pipeline.steps.parallelrunconfig). 
 
 
 The following diagram shows the workflow for the many models solution.