Merge pull request #85100 from PeterCLu/plu-amls-happy-path-links

AMLs Add links to Azure architecture center

Author: PRMerger10

articles/machine-learning/service/how-to-consume-web-service.md

@@ -493,3 +493,7 @@ Power BI supports consumption of Azure Machine Learning web services to enrich t
 To generate a web service that's supported for consumption in Power BI, the schema must support the format that's required by Power BI. [Learn how to create a Power BI-supported schema](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-and-where#example-script-with-dictionary-input-support-consumption-from-power-bi).
 
 Once the web service is deployed, it's consumable from Power BI dataflows. [Learn how to consume an Azure Machine Learning web service from Power BI](https://docs.microsoft.com/power-bi/service-machine-learning-integration).
+
+## Next steps
+
+To view a reference architecture for real-time scoring of Python and deep learning models, go to the [Azure architecture center](/azure/architecture/reference-architectures/ai/realtime-scoring-python).

articles/machine-learning/service/how-to-run-batch-predictions.md

@@ -354,7 +354,4 @@ RunDetails(published_pipeline_run).show()
 
 ## Next steps
 
-To see this working end-to-end, try the batch scoring notebook in [GitHub](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/machine-learning-pipelines).
-
-[!INCLUDE [aml-clone-in-azure-notebook](../../../includes/aml-clone-for-examples.md)]
-
+To see this working end-to-end, try the batch scoring notebook in [GitHub](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/machine-learning-pipelines), or go to the [Azure architecture center](/azure/architecture/reference-architectures/ai/batch-scoring-python) to see a sample solution architecture.

articles/machine-learning/service/how-to-train-chainer.md

@@ -211,3 +211,5 @@ In this article, you trained and registered a deep learning, neural network usin
 * [Tune hyperparameters](how-to-tune-hyperparameters.md)
 
 * [Track run metrics during training](how-to-track-experiments.md)
+
+* [View our reference architecture for distributed deep learning training in Azure](/azure/architecture/reference-architectures/ai/training-deep-learning)

articles/machine-learning/service/how-to-train-keras.md

@@ -204,3 +204,7 @@ In this article, you trained and registered a Keras model on Azure Machine Learn
 
 > [!div class="nextstepaction"]
 > [How and where to deploy models](how-to-deploy-and-where.md)
+* [Track run metrics during training](how-to-track-experiments.md)
+* [Tune hyperparameters](how-to-tune-hyperparameters.md)
+* [Deploy a trained model](how-to-deploy-and-where.md)
+* [Reference architecture for distributed deep learning training in Azure](/azure/architecture/reference-architectures/ai/training-deep-learning)

articles/machine-learning/service/how-to-train-pytorch.md

@@ -226,3 +226,4 @@ In this article, you trained and registered a deep learning, neural network usin
 * [Track run metrics during training](how-to-track-experiments.md)
 * [Tune hyperparameters](how-to-tune-hyperparameters.md)
 * [Deploy a trained model](how-to-deploy-and-where.md)
+* [Reference architecture for distributed deep learning training in Azure](/azure/architecture/reference-architectures/ai/training-deep-learning)

articles/machine-learning/service/how-to-train-scikit-learn.md

@@ -189,12 +189,12 @@ model = run.register_model(model_name='sklearn-iris', model_path='model.joblib')
 
 ## Next steps
 
-In this article, you trained and registered a machine learning, classification model using scikit-learn on Azure Machine Learning service.
 
-* To learn how to deploy a model, continue on to our [model deployment](how-to-deploy-and-where.md) article.
+In this article, you trained and registered a Keras model on Azure Machine Learning service. To learn how to deploy a model, continue on to our model deployment article.
 
-* [Tune hyperparameters](how-to-tune-hyperparameters.md).
-
-* [Track run metrics during training](how-to-track-experiments.md).
-
-* Learn more about [deep learning vs machine learning](concept-deep-learning-vs-machine-learning.md).
+> [!div class="nextstepaction"]
+> [How and where to deploy models](how-to-deploy-and-where.md)
+* [Track run metrics during training](how-to-track-experiments.md)
+* [Tune hyperparameters](how-to-tune-hyperparameters.md)
+* [Deploy a trained model](how-to-deploy-and-where.md)
+* [Reference architecture for distributed deep learning training in Azure](/azure/architecture/reference-architectures/ai/training-deep-learning)

articles/machine-learning/service/how-to-train-tensorflow.md

@@ -289,5 +289,9 @@ cluster_spec = tf.train.ClusterSpec(cluster)
 
 In this article, you trained and registered a TensorFlow model. To learn how to deploy a model to a GPU-enabled cluster, continue on to our GPU model deployment article.
 
-[How to deploy for inferencing with GPUs](how-to-deploy-inferencing-gpus.md)
-[How to monitor with TensorBoard](how-to-monitor-tensorboard.md)
+> [!div class="nextstepaction"]
+> [How and where to deploy models](how-to-deploy-and-where.md)
+* [Track run metrics during training](how-to-track-experiments.md)
+* [Tune hyperparameters](how-to-tune-hyperparameters.md)
+* [Deploy a trained model](how-to-deploy-and-where.md)
+* [Reference architecture for distributed deep learning training in Azure](/azure/architecture/reference-architectures/ai/training-deep-learning)