Merge pull request #108229 from PeterCLu/plu-comparison-update

v-shils · web-flow · commit f01989c4d891 · 2020-03-27T11:01:13.000-07:00
Update azure ml vs studio classic article
diff --git a/articles/machine-learning/compare-azure-ml-to-studio-classic.md b/articles/machine-learning/compare-azure-ml-to-studio-classic.md
@@ -1,54 +1,51 @@
 ---
 title: Azure Machine Learning vs. Machine Learning Studio (classic)  
-description: How Azure Machine Learning is different from Machine Learning Studio (classic)
+description: What's the difference between Azure Machine Learning and Machine Learning Studio (classic)?
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
 ms.topic: overview
 author: j-martens
 ms.author: jmartens
-ms.date: 10/29/2019
+ms.date: 03/25/2020
 ---
-
  
-# How Azure Machine Learning differs from Machine Learning Studio (classic)
+# Azure Machine Learning vs Machine Learning Studio (classic)
 
-This article compares the features, capabilities, and interface of Azure Machine Learning to Machine Learning Studio (classic). 
+In this article, you learn the difference between Azure Machine Learning and Machine Learning Studio (classic). 
 
-## About Machine Learning Studio (classic)
-[Machine Learning Studio (classic)](studio/what-is-ml-studio.md) is a collaborative, drag-and-drop visual workspace where you can build, test, and deploy machine learning solutions without needing to write code. It uses prebuilt and preconfigured machine learning algorithms and data-handling modules as well as a proprietary compute platform.
+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.
 
-## About Azure Machine Learning
+We recommend that new users choose Azure Machine Learning for the widest range of cutting-edge machine learning tools.
 
-Meanwhile, [Azure Machine Learning](overview-what-is-azure-ml.md) provides both a web interface called the designer (preview) **and** several SDKs and CLI to quickly prep data, train and deploy machine learning models. With Azure Machine Learning you get scale, multiple framework support, advanced ML capabilities like automated machine learning and pipeline support.
+## Quick comparison
 
-Azure Machine Learning designer provides a similar drag-and-drop experience to Studio (classic). However, unlike the proprietary compute platform of Studio (classic), the designer uses your own compute resources, is scalable, and is fully integrated into Azure Machine Learning.  
+The following table summarizes some of the key differences between Azure Machine Learning and Studio (classic):
 
-> [!TIP]
-> Customers currently using or evaluating Machine Learning Studio (classic) are encouraged to try [Azure Machine Learning designer](https://docs.microsoft.com/azure/machine-learning/concept-designer) (preview), which provides drag and drop ML modules __plus__ scalability, version control, and enterprise security.
+| | Machine Learning Studio (classic) | Azure Machine Learning |
+|---| --- | --- |
+| 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) |
 
-## Comparison: Azure Machine Learning vs. Machine Learning Studio (classic)
 
-Here is a quick comparison.
+## Migrate from Machine Learning Studio (classic)
 
-||  Azure Machine Learning designer|Studio (classic) |
-|---| --- | --- |
-||The designer is in preview, Azure Machine Learning is GA|Generally available (GA) | 
-|Drag-and-drop interface| Yes | Yes|
-|Experiment| Scale with compute target|Scale (10GB training data limit) | 
-|Modules for interface| [Many popular modules](algorithm-module-reference/module-reference.md) | Many |
-|Training compute targets| AML Compute(GPU/CPU)|Proprietary compute target, CPU only|
-|Inferencing compute targets| Azure Kubernetes Service for real-time inference <br/>AML Compute for batch inference|Proprietary web service format, not customizable | 
-|ML Pipeline| Pipeline authoring <br/> Published pipeline <br/> Pipeline endpoint <br/> [Learn more about ML pipeline](concept-ml-pipelines.md)|Not supported | 
-|ML Ops| Configurable deployment, model and pipeline versioning|Basic model management and deployment | 
-|Model| Standard format, various depends on the training job|Proprietary, non portable format.| 
-|Automated model training|Not yet in the designer, but possible through the interface and SDKs.| No | 
+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](overview-what-is-azure-ml.md).
 
-- Read the [Azure Machine Learning overview](tutorial-first-experiment-automated-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.
 
@@ -59,6 +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)
 
diff --git a/articles/machine-learning/studio/what-is-ml-studio.md b/articles/machine-learning/studio/what-is-ml-studio.md
@@ -11,19 +11,20 @@ ms.assetid: e65c8fe1-7991-4a2a-86ef-fd80a7a06269
 ms.service: machine-learning
 ms.subservice: studio
 ms.topic: overview
-ms.date: 10/17/2019
+ms.date: 03/24/2020
 ---
 # What is Machine Learning Studio (classic)?
 
-[!INCLUDE [Notebook deprecation notice](../../../includes/aml-studio-notebook-notice.md)]
-
 [!INCLUDE [Designer notice](../../../includes/designer-notice.md)]
 
 Microsoft Azure Machine Learning Studio (classic) is a collaborative, drag-and-drop tool you can use to build, test, and deploy predictive analytics solutions on your data. Azure Machine Learning Studio (classic) publishes models as web services that can easily be consumed by custom apps or BI tools such as Excel.
 
 Machine Learning Studio (classic) is where data science, predictive analytics, cloud resources, and your data meet.
 
 ## The Machine Learning Studio (classic)  interactive workspace
+
+[!INCLUDE [Notebook deprecation notice](../../../includes/aml-studio-notebook-notice.md)]
+
 To develop a predictive analysis model, you typically use data from one or more sources, transform, and analyze that data through various data manipulation and statistical functions, and generate a set of results. Developing a model like this is an iterative process. As you modify the various functions and their parameters, your results converge until you are satisfied that you have a trained, effective model.
 
 Azure Machine Learning Studio (classic) gives you an interactive, visual workspace to easily build, test, and iterate on a predictive analysis model. You drag-and-drop ***datasets*** and analysis ***modules*** onto an interactive canvas, connecting them together to form an ***experiment***, which you run in Machine Learning Studio (classic). To iterate on your model design, you edit the experiment, save a copy if desired, and run it again. When you're ready, you can convert your ***training experiment*** to a ***predictive experiment***, and then publish it as a ***web service*** so that your model can be accessed by others.
@@ -32,30 +33,6 @@ There is no programming required, visually connect datasets and modules to const
 
 ![Azure Machine Learning Studio (classic) diagram: Create experiments, read data for many sources, write scored data, write models.](./media/what-is-ml-studio/azure-ml-studio-diagram.jpg)
 
-<a name="compare"></a>
-## How does Machine Learning Studio (classic) differ from Azure Machine Learning?
-
-[Azure Machine Learning](../overview-what-is-azure-ml.md) provides both SDKs **-and-** the Azure Machine Learning designer (preview), to quickly prep data, train and deploy machine learning models. The designer provides a similar drag-and-drop experience to Studio (classic). However, unlike the proprietary compute platform of Studio (classic), the designer uses your own compute resources and is fully integrated into Azure Machine Learning.
-
-Here is a quick comparison:
-
-|| Machine Learning Studio (classic) | Azure Machine Learning |
-|---| --- | --- |
-| Drag and drop interface | Yes | Yes - [Azure Machine Learning designer (preview)](../concept-designer.md) |
-| Experiment | Scalable (10-GB training data limit) | Scale with compute target |
-| Modules for drag-and-drop interface | Many | Initial set of popular [modules](../algorithm-module-reference/module-reference.md)|
-|Training compute targets| Proprietary compute target, CPU support only| Supports Azure Machine Learning compute (GPU or CPU) and Notebook VMs.<br/>([Other computes supported in SDK](../concept-compute-target.md#train))|
-|Inferencing compute targets| Proprietary web service format, not customizable |  Azure Kubernetes Service and AML Compute <br/>([Other computes supported in SDK](../how-to-deploy-and-where.md)) |
-| ML Pipeline | Not supported | [Pipelines](../concept-ml-pipelines.md) supported |
-| MLOps | Basic model management and deployment | Configurable deployment - model and pipeline versioning and tracking |
-| Model format | Proprietary format, Studio (classic) only | Standard format depending on training job type |
-|Automated model training and hyperparameter tuning | No | Not yet in the designer <br/> ([Supported in the SDK and workspace landing page](../concept-automated-ml.md)) | 
-
-Try out the designer with [Tutorial: Predict automobile price with the designer](../tutorial-designer-automobile-price-train-score.md)
-
-> [!NOTE]
-> Models created in Studio (classic) can't be deployed or managed by Azure Machine Learning. However, models created and deployed in the designer can be managed through the Azure Machine Learning workspace.
-
 ## Download the Machine Learning Studio (classic) overview diagram
 Download the **Microsoft Azure Machine Learning Studio (classic) Capabilities Overview** diagram and get a high-level view of the capabilities of Machine Learning Studio (classic). To keep it nearby, you can print the diagram in tabloid size (11 x 17 in.).
 
diff --git a/includes/designer-notice.md b/includes/designer-notice.md
@@ -7,8 +7,10 @@ ms.custom: "include file"
 ms.topic: "include"
 author: nibaccam
 ms.author: nibaccam
-ms.date: 11/04/2019
+ms.date: 03/20/2020
 ---
 
 > [!TIP]
-> Customers currently using or evaluating Machine Learning Studio (classic) are encouraged to try [Azure Machine Learning designer](https://docs.microsoft.com/azure/machine-learning/concept-designer) (preview), which provides drag-n-drop ML modules __plus__ scalability, version control, and enterprise security.
+> Customers currently using or evaluating Machine Learning Studio (classic) are encouraged to try [Azure Machine Learning designer](https://docs.microsoft.com/azure/machine-learning/concept-designer) (preview), which provides drag and drop ML modules __plus__ scalability, version control, and enterprise security.
+>
+>To learn more, see [Azure Machine Learning vs Machine Learning Studio (classic)](../articles/machine-learning/compare-azure-ml-to-studio-classic.md).
