Feedback

Author: PeterCLu

articles/machine-learning/compare-azure-ml-to-studio-classic.md

@@ -7,43 +7,45 @@ ms.subservice: core
 ms.topic: overview
 author: j-martens
 ms.author: jmartens
-ms.date: 03/24/2020
+ms.date: 03/25/2020
 ---
 
 # Azure Machine Learning vs Machine Learning Studio (classic)
 
-You can use both Azure Machine Learning and Machine Learning Studio (classic) to build and deploy machine learning models. In this article, you learn the differences between the two offerings.
+In this article, you learn the difference between Azure Machine Learning and Machine Learning Studio (classic). 
 
-Azure Machine Learning provides both SDKs **and** the "drag-and-drop" designer to build, deploy, and manage machine learning models. Studio (classic) only offers a standalone drag-and-drop experience.
+Azure Machine Learning provides Python and R SDKs **and** the "drag-and-drop" designer to build and deploy machine learning models. Studio (classic) only offers a standalone drag-and-drop experience.
 
-We recommend that new users start with Azure Machine Learning for a comprehensive set of the most cutting-edge machine learning tools. For more information on what Azure Machine Learning has to offer, see [What is Azure Machine Learning?](overview-what-is-azure-ml.md)
+We recommend that new users choose Azure Machine Learning for the widest range of cutting-edge machine learning tools.
 
 ## Quick comparison
 
-The following table summarizes some of the key differences betwee Azure Machine Learning and Studio (classic):
+The following table summarizes some of the key differences between Azure Machine Learning and Studio (classic):
 
-| | Azure Machine Learning | Machine Learning Studio (classic) | 
+| | Machine Learning Studio (classic) | Azure Machine Learning |
 |---| --- | --- |
-| Drag and drop interface | Supported - [Azure Machine Learning designer (preview)](concept-designer.md) | Supported | 
-| Experiment | Scale with compute target | Scalable (10-GB training data limit) |
-| Training compute targets | Supports Azure Machine Learning compute (GPU or CPU) and Notebook VMs.<br/>([Other computes supported in SDK](concept-compute-target.md#train))| Proprietary compute target, CPU support only|
-| Inferencing compute targets | Azure Kubernetes Service and AML Compute <br/>([Other computes supported in SDK](how-to-deploy-and-where.md)) | Proprietary web service format, not customizable |
-| ML Pipeline | [Supported](concept-ml-pipelines.md) | Not supported |
-| MLOps | [Configurable deployment](concept-model-management-and-deployment.md) - model, pipeline, and dataset versioning and tracking | Basic model management and deployment |
-| Model format | Standard format depending on training job type | Proprietary format, Studio (classic) only |
-| Automated model training and hyperparameter tuning | [Supported in the SDK and visual workspace](concept-automated-ml.md) | Not supported | 
-| Data drift detection | [Supported in SDK and visual workspace](how-to-monitor-datasets.md)  | Not supported |
+| Drag and drop interface | Supported | Supported - [Azure Machine Learning designer (preview)](concept-designer.md) | 
+| Experiment | Scalable (10-GB training data limit) | Scale with compute target |
+| Training compute targets | Proprietary compute target, CPU support only | Wide range of customizable [training compute targets](concept-compute-target.md#train). Includes GPU and CPU support | 
+| Deployment compute targets | Proprietary web service format, not customizable | Wide range of customizable [deployment compute targets](concept-compute-target.md#deploy). Includes GPU and CPU support |
+| ML Pipeline | Not supported | Build flexible, modular [pipelines](concept-ml-pipelines.md) to automate workflows |
+| MLOps | Basic model management and deployment | Entity versioning (model, data, workflows), workflow automation, integration with CICD tooling, [and more](concept-model-management-and-deployment.md) |
+| Model format | Proprietary format, Studio (classic) only | Multiple supported formats depending on training job type |
+| Automated model training and hyperparameter tuning |  Not supported | [Supported in the SDK and visual workspace](concept-automated-ml.md) | 
+| Data drift detection | Not supported | [Supported in SDK and visual workspace](how-to-monitor-datasets.md) |
 
 
 ## Migrate from Machine Learning Studio (classic)
 
-Currently, there's no way to migrate Studio (classic) assets to Azure Machine Learning designer (preview). However, we'll provide a migration path once the designer becomes generally available. Until then, we encourage users to try the designer, which provides a familiar drag-and-drop experience with improved workflow **plus** scalability, version control, and enterprise security.
+Currently, there's no way to migrate Studio (classic) assets to Azure Machine Learning designer (preview). Current Studio (classic) users can continue to use their machine learning assets. However, we encourage all users to considering using the designer, which provides a familiar drag-and-drop experience with improved workflow **plus** scalability, version control, and enterprise security.
 
 ## Get started with Azure Machine Learning
 
-The following resources can help you get started with Azure Machine Learning
+The following resources can help you get started with Azure Machine Learning. 
 
-- Read the [Azure Machine Learning overview](tutorial-first-experiment-automated-ml.md) 
+- Read the [Azure Machine Learning overview](overview-what-is-azure-ml.md).
+
+- Create your [first experiment with the Python SDK](tutorial-1st-experiment-sdk-setup.md).
 
 - [Create your first designer pipeline](tutorial-designer-automobile-price-train-score.md) to predict auto prices.
 
@@ -54,6 +56,6 @@ The following resources can help you get started with Azure Machine Learning
 In addition to the drag-and-drop capabilities in the designer, Azure Machine Learning has other tools available:  
   + [Use Python notebooks to train & deploy ML models](tutorial-1st-experiment-sdk-setup.md)
   + [Use R Markdown to train & deploy ML models](tutorial-1st-r-experiment.md) 
-  + [Use automated machine learning to train & deploy ML models](tutorial-designer-automobile-price-train-score.md) 
+  + [Use automated machine learning to train & deploy ML models](tutorial-first-experiment-automated-ml.md)  
   + [Use the machine learning CLI to train and deploy a model](tutorial-train-deploy-model-cli.md)