add parallelism limit

Author: iamrk04

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

@@ -15,9 +15,7 @@ show_latex: true
 #customer intent: As a data scientist, I want to understand model inference and evaluation in forecasting tasks.
 ---
 
-# Inference and evaluation of forecasting models (preview)
-
-[!INCLUDE [machine-learning-preview-generic-disclaimer](./includes/machine-learning-preview-generic-disclaimer.md)]
+# Inference and evaluation of forecasting models
 
 This article introduces concepts related to model inference and evaluation in forecasting tasks. For instructions and examples for training forecasting models in AutoML, see [Set up AutoML to train a time-series forecasting model with SDK and CLI](./how-to-auto-train-forecast.md).
 

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

@@ -1318,6 +1318,9 @@ For a more detailed example, see the [demand forecasting with many models notebo
 > [!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.  
 
+> [!NOTE]
+> The default parallelism limit for a many models run within a subscription is set to 320. If your workload requires a higher limit, please don't hesitate to reach out to us.
+
 <a name="forecasting-at-scale-hierarchical-time-series"></a>
 
 ## Forecast at scale: hierarchical time series
@@ -1594,6 +1597,9 @@ For a more detailed example, see the [demand forecasting with hierarchical time
 > [!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.
 
+> [!NOTE]
+> The default parallelism limit for a hierarchical time series run within a subscription is set to 320. If your workload requires a higher limit, please don't hesitate to reach out to us.
+
 ## Forecast at scale: distributed DNN training
 
 - To learn how distributed training works for forecasting tasks, see [Distributed DNN training](concept-automl-forecasting-at-scale.md#distributed-dnn-training-preview).