Building Azure Functions using ML.NET models
Once a machine learning model has been built, it can be consumed from any kind of .NET application, such as a WPF, Web Application, Web API, or even from microservices. In this section, we will use ML.NET to build a sentiment analysis service and then integrate it into an Azure function. This will replace the rudimentary sentiment analysis service that we built for the same purpose in the previous chapter.
We will then see the improved sentiment analysis service being used from the Azure function, which itself is being consumed by the BookApp web application we also built in the previous chapter.
Azure Functions and serverless architecture
If you are not familiar with Azure Functions, you can read an introduction to it in the Deploying serverlessly – Azure Functions section of Chapter 6, Upgrading On-Premises Applications to the Cloud with .NET 5. There is also information about serverless architecture in general in the...
