Merge pull request #213180 from Blackmist/misc-v2

misc updates for v2

Author: v-dirichards

articles/machine-learning/media/concept-environments/ml-environment.png

@@ -8,25 +8,21 @@ ms.author: sgilley
 manager: cgronlund
 ms.custom: "include file"
 ms.topic: "include"
-ms.date: 10/21/2021
+ms.date: 09/30/2022
 ---
 
 
 The compute target you use to host your model will affect the cost and availability of your deployed endpoint. Use this table to choose an appropriate compute target.
 
 | Compute target | Used for | GPU support | Description |
 | ----- | ----- | ----- | ----- | 
-| [Local web service](../articles/machine-learning/v1/how-to-deploy-local-container-notebook-vm.md) | Testing/debugging |    | Use for limited testing and troubleshooting. Hardware acceleration depends on use of libraries in the local system.
+| [Local web service](../articles/machine-learning/v1/how-to-deploy-local-container-notebook-vm.md) | Testing/debugging |    | Use for limited testing and troubleshooting. Hardware acceleration depends on use of libraries in the local system. |
+| [Azure Machine Learning endpoints](../articles/machine-learning/concept-endpoints.md) | Real-time inference <br/><br/>Batch&nbsp;inference | Yes |  Fully managed computes for real-time (managed online endpoints) and batch scoring (batch endpoints) on serverless compute. |
 | [Azure Machine Learning Kubernetes](../articles/machine-learning/how-to-attach-kubernetes-anywhere.md) | Real-time inference <br/><br/> Batch inference | Yes | Run inferencing workloads on on-premises, cloud, and edge Kubernetes clusters. |  
-| [Azure Container Instances](../articles/machine-learning/v1/how-to-deploy-azure-container-instance.md) | Real-time inference <br/><br/> Recommended for dev/test purposes only.| &nbsp;  | Use for low-scale CPU-based workloads that require less than 48 GB of RAM. Doesn't require you to manage a cluster. <br/><br/> Supported in the designer. |
-| [Azure Machine Learning compute clusters](../articles/machine-learning/tutorial-pipeline-batch-scoring-classification.md) | Batch&nbsp;inference | [Yes](../articles/machine-learning/tutorial-pipeline-batch-scoring-classification.md) (machine learning pipeline) |  Run batch scoring on serverless compute. Supports normal and low-priority VMs. No support for real-time inference.|
+| [Azure Container Instances](../articles/machine-learning/v1/how-to-deploy-azure-container-instance.md) (SDK/CLI v1 only) | Real-time inference <br/><br/> Recommended for dev/test purposes only.| &nbsp;  | Use for low-scale CPU-based workloads that require less than 48 GB of RAM. Doesn't require you to manage a cluster. <br/><br/> Supported in the designer. |
 
 > [!NOTE]
-> Although compute targets like local, and Azure Machine Learning compute clusters support GPU for training and experimentation, using GPU for inference _when deployed as a web service_ is supported only on Azure Machine Learning Kubernetes.
->
-> Using a GPU for inference _when scoring with a machine learning pipeline_ is supported only on Azure Machine Learning compute.
-> 
 > When choosing a cluster SKU, first scale up and then scale out. Start with a machine that has 150% of the RAM your model requires, profile the result and find a machine that has the performance you need. Once you've learned that, increase the number of machines to fit your need for concurrent inference.
 
 > [!NOTE]
-> * Container instances are suitable only for small models less than 1 GB in size.
+> Container instances require the SDK or CLI v1 and are suitable only for small models less than 1 GB in size.