Merge pull request #280834 from Blackmist/265990-fpga

removing fpga article per PM conversation

Author: JamesJBarnett

articles/machine-learning/.openpublishing.redirection.machine-learning.json

@@ -172,7 +172,7 @@
     },
     {
         "source_path_from_root": "/articles/machine-learning/v-fake/how-to-deploy-fpga-web-service.md",
-        "redirect_url": "/azure/machine-learning/how-to-deploy-fpga-web-service",
+        "redirect_url": "/azure/machine-learning/how-to-deploy-online-endpoints",
         "redirect_document_id": false
     },
     {

articles/machine-learning/reference-machine-learning-cloud-parity.md

@@ -84,7 +84,6 @@ The information in the rest of this document provides information on what featur
 | **Machine learning lifecycle** |   | | |
 | [Model profiling (SDK/CLI v1)](v1/how-to-deploy-profile-model.md)                                                            | GA                   | YES                | PARTIAL     |
 | [The Azure Machine Learning CLI v1](v1/reference-azure-machine-learning-cli.md)     | GA                   | YES                | YES         |
-| [FPGA-based Hardware Accelerated Models (SDK/CLI v1)](./v1/how-to-deploy-fpga-web-service.md)                                     | GA                   | NO                 | NO          |
 | [Visual Studio Code integration](how-to-setup-vs-code.md)                                             | Public Preview       | NO                 | NO          |
 | [Event Grid integration](how-to-use-event-grid.md)                                                     | Public Preview       | NO                 | NO          |
 | [Integrate Azure Stream Analytics with Azure Machine Learning](../stream-analytics/machine-learning-udf.md)               | Public Preview       | NO                 | NO          |
@@ -106,7 +105,6 @@ The information in the rest of this document provides information on what featur
 | **Inference** |   | | |
 | Managed online endpoints | GA | YES | YES |
 | [Batch inferencing](tutorial-pipeline-batch-scoring-classification.md)                                                          | GA                   | YES                | YES         |
-| [Azure Stack Edge with FPGA (SDK/CLI v1)](./v1/how-to-deploy-fpga-web-service.md#deploy-to-a-local-edge-server)                                                    | Public Preview       | NO                 | NO          |
 | **Other** |   | | |
 | [Open Datasets](../open-datasets/samples.md)                                                              | Public Preview       | YES                | YES         |
 | [Custom Azure AI Search (SDK v1)](./v1/how-to-deploy-model-cognitive-search.md)                                                    | Public Preview       | YES                | YES         |

articles/machine-learning/toc.yml

@@ -1036,8 +1036,6 @@
           href: ./v1/how-to-deploy-inferencing-gpus.md
         - name: Azure AI Search
           href: ./v1/how-to-deploy-model-cognitive-search.md
-        - name: FPGA inference
-          href: ./v1/how-to-deploy-fpga-web-service.md
     - name: Hyperparameter tuning a model (v1)
       displayName: azurecli, AzureML
       href: ./v1/how-to-tune-hyperparameters.md

articles/machine-learning/v1/.openpublishing.redirection.machine-learning-v1.json

@@ -5,6 +5,11 @@
         "redirect_url": "/azure/machine-learning/how-to-set-up-training-targets",
         "redirect_document_id": false
     },
+    {
+        "source_path_from_root": "/articles/machine-learning/v1/how-to-deploy-fpga-web-service.md",
+        "redirect_url": "/azure/machine-learning/how-to-deploy-online-endpoints",
+        "redirect_document_id": false
+    },
     {
         "source_path_from_root": "/articles/machine-learning/v1/tutorial-1st-experiment-bring-data.md",
         "redirect_url": "/azure/machine-learning/tutorial-train-deploy-notebook",

articles/machine-learning/v1/concept-azure-machine-learning-architecture.md

@@ -204,7 +204,7 @@ An endpoint is an instantiation of your model into a web service that can be hos
 
 #### Web service endpoint
 
-When deploying a model as a web service, the endpoint can be deployed on Azure Container Instances, Azure Kubernetes Service, or FPGAs. You create the service from your model, script, and associated files. These are placed into a base container image, which contains the execution environment for the model. The image has a load-balanced, HTTP endpoint that receives scoring requests that are sent to the web service.
+When deploying a model as a web service, the endpoint can be deployed on Azure Container Instances or Azure Kubernetes Service. You create the service from your model, script, and associated files. These are placed into a base container image, which contains the execution environment for the model. The image has a load-balanced, HTTP endpoint that receives scoring requests that are sent to the web service.
 
 You can enable Application Insights telemetry or model telemetry to monitor your web service. The telemetry data is accessible only to you.  It's stored in your Application Insights and storage account instances. If you've enabled automatic scaling, Azure automatically scales your deployment.
 

articles/machine-learning/v1/concept-model-management-and-deployment.md

@@ -98,7 +98,7 @@ For more information on ONNX with Azure Machine Learning, see the [Create and ac
 
 ### Use models
 
-Trained machine learning models are deployed as web services in the cloud or locally. Deployments use CPU, GPU, or field-programmable gate arrays (FPGA) for inferencing. You can also use models from Power BI.
+Trained machine learning models are deployed as web services in the cloud or locally. Deployments use CPU, or GPU for inferencing. You can also use models from Power BI.
 
 When using a model as a web service, you provide the following items:
 

articles/machine-learning/v1/how-to-consume-web-service.md

@@ -21,7 +21,7 @@ ms.custom: UpdateFrequency5, devx-track-python, devx-track-csharp, cliv1, sdkv1,
 
 Deploying an Azure Machine Learning model as a web service creates a REST API endpoint. You can send data to this endpoint and receive the prediction returned by the model. In this document, learn how to create clients for the web service by using C#, Go, Java, and Python.
 
-You create a web service when you deploy a model to your local environment, Azure Container Instances, Azure Kubernetes Service, or field-programmable gate arrays (FPGA). You retrieve the URI used to access the web service by using the [Azure Machine Learning SDK](/python/api/overview/azure/ml/intro). If authentication is enabled, you can also use the SDK to get the authentication keys or tokens.
+You create a web service when you deploy a model to your local environment, Azure Container Instances, or Azure Kubernetes Service. You retrieve the URI used to access the web service by using the [Azure Machine Learning SDK](/python/api/overview/azure/ml/intro). If authentication is enabled, you can also use the SDK to get the authentication keys or tokens.
 
 The general workflow for creating a client that uses a machine learning web service is: