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learn-pr/wwl-data-ai/get-tips-tricks-for-teaching-dp-100-designing-implementing-data-science-solution/includes/14-explore-best-practices-dp-100-learning-path-11.md

@@ -2,7 +2,7 @@ DP-100: Learning Path 11 Design an MLOps solution
 
 Learning Path 11 Overview
 
-As you begin to teach this learning path, get familiar with what the students will learn during the learning path. In this learning path students will learn about designing a machine learning operations solution.
+As you begin to teach this learning path, get familiar with what the students learn during the learning path. In this learning path students learn about designing a machine learning operations solution.
 
 This learning path consists of five focus areas:
 

learn-pr/wwl-data-ai/get-tips-tricks-for-teaching-dp-100-designing-implementing-data-science-solution/includes/5-explore-best-practices-dp-100-learning-path-2.md

@@ -22,7 +22,7 @@ Learning path 2 Tips and Tricks
 
 Learning path 2 Demos
 
-- Demo the Azure Machine Learning workspace in Azure Portal
+- Demo the Azure Machine Learning workspace in Azure portal
 - Briefly show the AutoML and/or designer pipeline
 - Demo one of these for Azure Machine Learning: CLI / SDK / ARM Templates
 

learn-pr/wwl-data-ai/get-tips-tricks-for-teaching-dp-100-designing-implementing-data-science-solution/includes/6-explore-best-practices-dp-100-learning-path-3.md

@@ -2,7 +2,7 @@ DP-100: Learning Path 3 Make data available in Azure Machine Learning
 
 Learning Path 3 Overview
 
-As you begin to teach this learning path, get familiar with what the students will learn during the learning path. In this learning path students will learn how to make data available in Azure Machine Learning by emphasizing the fact that data happens to be the fundamental element in any machine learning workload. Students will also learn about creating and managing the datastores and data assets, how to use them in the model training experiments, and manage them in Azure Machine Learning workspace.
+As you begin to teach this learning path, get familiar with what the students learn during the learning path. In this learning path students learn how to make data available in Azure Machine Learning by emphasizing the fact that data happens to be the fundamental element in any machine learning workload. Students will also learn about creating and managing the datastores and data assets, how to use them in the model training experiments, and manage them in Azure Machine Learning workspace.
 
 This learning path consists of three focus areas:
 
@@ -16,7 +16,7 @@ Learning Path 3 Tips and Tricks
 
 - Begin this topic with learner engagement (create a question around storage)
 - Incorporate whiteboarding or diagrams as needed
-- Mention the three protocols supported: http(s), abfs(s), and azure ml)
+- Mention the three protocols supported: http(s), abfs(s), and azure(ml)
 - Discuss the variety of data sources supported
 
 Learning Path 3 Demos (optional)

learn-pr/wwl-data-ai/get-tips-tricks-for-teaching-dp-100-designing-implementing-data-science-solution/includes/9-explore-best-practices-dp-100-learning-path-6.md

@@ -15,7 +15,7 @@ This learning path consists of four focus areas:
 
 Learning Path 6 Tips and Tricks
 
-- Describe an Mlflow solution overview with a good architecture diagram
+- Describe a Mlflow solution overview with a good architecture diagram
 - Revisit the full iterative process to show model choice process in more details
 - Provide a code walk-through for classes, objects, and methods necessary