Update aml-compute-target-deploy.md

remove FPGA (empty column)

Author: sdgilley

includes/aml-compute-target-deploy.md

@@ -14,12 +14,12 @@ ms.date: 10/21/2021
 
 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 | FPGA 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.
-| [Azure Machine Learning Kubernetes](../articles/machine-learning/how-to-attach-kubernetes-anywhere.md) | Real-time inference <br/><br/> Batch inference | Yes | N/A | 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;  | &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) | &nbsp;  | Run batch scoring on serverless compute. Supports normal and low-priority VMs. No support for real-time inference.|
+| Compute target | Used for | GPU support | Description |
+| ----- | ----- | ----- | ----- | 
+| [Local&nbsp;web&nbsp;service](../articles/machine-learning/v1/how-to-deploy-local-container-notebook-vm.md) | Testing/debugging |  &nbsp; | Use for limited testing and troubleshooting. Hardware acceleration depends on use of libraries in the local system.
+| [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.|
 
 > [!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.
@@ -29,4 +29,4 @@ The compute target you use to host your model will affect the cost and availabil
 > 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 are suitable only for small models less than 1 GB in size.