Update concept-automl-forecasting-at-scale.md

Author: ssalgadodev

articles/machine-learning/concept-automl-forecasting-at-scale.md

@@ -10,15 +10,15 @@ ms.service: azure-machine-learning
 ms.subservice: automl
 ms.topic: conceptual
 ms.custom: automl, sdkv2
-ms.date: 08/01/2023
+ms.date: 08/21/2024
 show_latex: true
 ---
 
 # Forecasting at scale: many models and distributed training
 
 This article is about training forecasting models on large quantities of historical data. Instructions and examples for training forecasting models in AutoML can be found in our [set up AutoML for time series forecasting](./how-to-auto-train-forecast.md) article.
 
-Time series data can be large due to the number of series in the data, the number of historical observations, or both. **Many models** and hierarchical time series, or **HTS**, are scaling solutions for the former scenario, where the data consists of a large number of time series. In these cases, it can be beneficial for model accuracy and scalability to partition the data into groups and train a large number of independent models in parallel on the groups. Conversely, there are scenarios where one or a small number of high-capacity models is better. **Distributed DNN training** targets this case. We review concepts around these scenarios in the remainder of the article. 
+Time series data can be large due to the number of series in the data, the number of historical observations, or both. **Many models** and hierarchical time series, or **HTS**, are scaling solutions for the former scenario, where the data consists of a large number of time series. In these cases, it can be beneficial for model accuracy and scalability to partition the data into groups and train a large number of independent models in parallel on the groups. Conversely, there are scenarios where one or a few high-capacity models are better. **Distributed DNN training** targets this case. We review concepts around these scenarios in the remainder of the article. 
 
 ## Many models