#AzureML – Creating a HTTP Rest Endpoint to consume an AzureML Designer based on the DenseNet template 🎨
Tags: englishpost, azuremachinelearning, azuremldataset
Published: 2022-01-26 20:59:34

---
title: #AzureML – Creating a HTTP Rest Endpoint to consume an AzureML Designer based on the DenseNet template 🎨
published: true
date: 2022-01-26 15:59:29 UTC
tags: EnglishPost,AzureMachineLearning,AzureMachineLearning,AzureMLDataSet
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---

<p>Hi !</p>



<p>In my previous post I wrote about how to create a custom dataset with images to be used on a Azure Machine Learning Designer project. The second post was about how to use the custom data set and how to train the model. Today let's publish the model to be used as a WebService / HTTP Rest endpoint.</p>



<p>Next step is to select the "real time inference pipeline" option from the [Create inference pipeline] option and press [Publish]. </p>



<figure class="wp-block-image size-large"><img src="https://brunocapuano.files.wordpress.com/2022/01/create-inference-pipeline.png?w=1024" alt="create inference pipeline" class="wp-image-35153"/></figure>



<p>This will create a new designer pipeline project, related to the original one. A closer look at the new pipeline, shows new elements in the designer area</p>



<ul><li>WebService Input</li><li>WebService Output</li></ul>



<p>And also, on top, we can see how both designer pipelines are related.</p>



<figure class="wp-block-image size-large"><img src="https://brunocapuano.files.wordpress.com/2022/01/new-inference-pipeline-with-webservice-input-and-output.png?w=1024" alt="new inference pipeline with webservice input and output" class="wp-image-35155"/></figure>



<p>Let's Submit this pipeline and wait for the completion of the process.</p>



<figure class="wp-block-image size-large"><img src="https://brunocapuano.files.wordpress.com/2022/01/realtime-inference-pipeline-run-finished.png?w=1024" alt="realtime inference pipeline run finished" class="wp-image-35159"/></figure>



<p><em><strong>Important: </strong>you can change the default names for WebServiceInput1 and WebServiceOutput1. This will help later with the JSON schema to consume the webservice.</em></p>



<p>Once the process is complete, we can deploy this running pipeline to be used as an HTTP REST Endpoint. I'll create a new endpoint named [squirrelimageclassification], and a Compute Instance to host this resource.</p>


<figure class="wp-block-image size-large"><img src="https://brunocapuano.files.wordpress.com/2022/01/setup-real-time-endpoint.png?w=670" alt="setup realtime endpoint" class="wp-image-35161"/></figure>



<p>A couple of minutes later, we are ready to go !</p>


<figure class="wp-block-image size-large"><img src="https://brunocapuano.files.wordpress.com/2022/01/new-endpoint-created.png?w=648" alt="new endpoint created" class="wp-image-35164"/></figure>



<p>When we open the new endpoint, we can review useful information like the HTTP endpoint !</p>


<figure class="wp-block-image size-large"><img src="https://brunocapuano.files.wordpress.com/2022/01/endpoint-details.png?w=563" alt="endpoint details" class="wp-image-35166"/></figure>



<p>And we even have a test section and sample code to test the Endpoint.</p>


<figure class="wp-block-image size-large"><img src="https://brunocapuano.files.wordpress.com/2022/01/sample-code-to-test-the-endpoint.png?w=730" alt="sample code to test the endpoint" class="wp-image-35167"/></figure>



<p>Tomorrow I'll show how to write a client desktop app to use the the generated endpoint!</p>

[Continue reading #AzureML – Creating a HTTP Rest Endpoint to consume an AzureML Designer based on the DenseNet template 🎨](https://elbruno.com/2022/01/26/azureml-creating-a-http-rest-endpoint-to-consume-an-azureml-designer-based-on-the-densenet-template-%f0%9f%8e%a8/)