Update how-to-auto-train-forecast.md

Author: iamrk04

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

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 ## Orchestrate training, inference, and evaluation with components and pipelines
 
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 Your ML workflow likely requires more than just training. Inference, or retrieving model predictions on newer data, and evaluation of model accuracy on a test set with known target values are other common tasks that you can orchestrate in Azure Machine Learning along with training jobs. To support inference and evaluation tasks, Azure Machine Learning provides [components](concept-component.md), which are self-contained pieces of code that do one step in an Azure Machine Learning [pipeline](concept-ml-pipelines.md).
 
 # [Python SDK](#tab/python)
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 ## Forecast at scale: many models
 
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 The many models components in AutoML enable you to train and manage millions of models in parallel. For more information on many models concepts, see [Many models](concept-automl-forecasting-at-scale.md#many-models).
 
 ### Many models training configuration
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 ## Forecast at scale: hierarchical time series
 
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 The hierarchical time series (HTS) components in AutoML enable you to train a large number of models on data with hierarchical structure. For more information, see the [HTS article section](concept-automl-forecasting-at-scale.md#hierarchical-time-series-forecasting).
 
 ### HTS training configuration