Merge pull request #113207 from MicrosoftDocs/j-martens-patch-18

Update tutorial-pipeline-batch-scoring-classification.md

Author: PRMerger6

articles/machine-learning/tutorial-convert-ml-experiment-to-production.md

@@ -1,15 +1,17 @@
 ---
-title: Convert machine learning experiment code to production code
+title: Convert notebook code into Python scripts 
 titleSuffix: Azure Machine Learning
-description: Learn how to convert machine learning experimental code to production code using the MLOpsPython code template. 
+description: Turn your machine learning experimental notebooks into production-ready code using the MLOpsPython code template. You can then test, deploy, and automate that code. 
 author: bjcmit
 ms.author: brysmith
 ms.service: machine-learning
 ms.topic: tutorial
-ms.date: 03/13/2020
+ms.date: 04/30/2020
 ---
 
-# Tutorial: Convert ML experimental code to production code
+# Tutorial: Convert ML experiments to production Python code
+
+In this tutorial, you learn how to convert Juptyer notebooks into Python scripts to make it testing and automation friendly using the MLOpsPython code template and Azure Machine Learning. Typically, this process is used to take experimentation / training code from a Juptyer notebook and convert it into Python scripts. Those scripts can then be used testing and CI/CD automation in your production environment. 
 
 A machine learning project requires experimentation where hypotheses are tested with agile tools like Jupyter Notebook using real datasets. Once the model is ready for production, the model code should be placed in a production code repository. In some cases, the model code must be converted to Python scripts to be placed in the production code repository. This tutorial covers a recommended approach on how to export experimentation code to Python scripts.
 

articles/machine-learning/tutorial-pipeline-batch-scoring-classification.md

@@ -16,7 +16,7 @@ ms.date: 03/11/2020
 
 [!INCLUDE [applies-to-skus](../../includes/aml-applies-to-basic-enterprise-sku.md)]
 
-Learn how to build a pipeline in Azure Machine Learning to run a batch scoring job. Machine learning pipelines optimize your workflow with speed, portability, and reuse, so you can focus on machine learning instead of infrastructure and automation. After you build and publish a pipeline, you configure a REST endpoint that you can use to trigger the pipeline from any HTTP library on any platform. 
+In this advanced tutorial, you learn how to build a pipeline in Azure Machine Learning to run a batch scoring job. Machine learning pipelines optimize your workflow with speed, portability, and reuse, so you can focus on machine learning instead of infrastructure and automation. After you build and publish a pipeline, you configure a REST endpoint that you can use to trigger the pipeline from any HTTP library on any platform. 
 
 The example uses a pretrained [Inception-V3](https://arxiv.org/abs/1512.00567) convolutional neural network model implemented in Tensorflow to classify unlabeled images. [Learn more about machine learning pipelines](concept-ml-pipelines.md).