Fix the link to dnn training

Author: arun-rajora

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

@@ -26,7 +26,7 @@ The many models [components](concept-component.md) in AutoML enable you to train
 
 :::image type="content" source="./media/how-to-auto-train-forecast/many-models.svg" alt-text="Diagram showing the AutoML many models workflow.":::
 
-The many models training component applies AutoML's [model sweeping and selection](concept-automl-forecasting-sweeping.md) independently to each store in this example. This model independence aids scalability and can benefit model accuracy especially when the stores have diverging sales dynamics. However, a single model approach may yield more accurate forecasts when there are common sales dynamics. See the [distributed DNN training](#distributed-dnn-training) section for more details on that case.
+The many models training component applies AutoML's [model sweeping and selection](concept-automl-forecasting-sweeping.md) independently to each store in this example. This model independence aids scalability and can benefit model accuracy especially when the stores have diverging sales dynamics. However, a single model approach may yield more accurate forecasts when there are common sales dynamics. See the [distributed DNN training](#distributed-dnn-training-preview) section for more details on that case.
 
 You can configure the data partitioning, the [AutoML settings](how-to-auto-train-forecast.md#configure-experiment) for the models, and the degree of parallelism for many models training jobs. For examples, see our guide section on [many models components](how-to-auto-train-forecast.md#forecasting-at-scale-many-models).