Azure ML Deploy Inference Endpoint
This video walks through the process of deploying a machine learning model to a REST endpoint using Microsoft Azure Machine Learning, covering all necessary steps from creating resources and launching ML Studio to training the model and registering it before finally deploying the inference endpoint. Viewers will learn how to integrate their trained model into production applications by making predictions available via HTTP requests.
Description
When a machine learning model is validated and effective at predicting outcomes, the next step is putting them to use in production applications. An excellent way to integrate AI and ML models with other applications is providing predictions as a standard web service endpoint--accepting JSON inputs and emitting predictions as JSON objects.
This video is a end-to-end tutorial showing how to deploy a Machine Learning model created in Microsoft Azure Machine Learning to a REST endpoint callable from another application via HTTP.
This video is based on the blog post https://robkerr.ai/deploy-azure-machine-learning-model-to-rest
00:00 - Introduction
00:39 - Create Resources
01:58 - Launch ML Studio
02:40 - Create Compute
04:15 - Upload Data
05:51 - Create Notebook
07:51 - Train Model
09:00 - Review Model
10:31 - Register Model
11:21 - Deploy Endpoint
14:32 - Fetch Bearer Token
16:12 - Test REST Endpoint