The goal is to be able to make SENTIMENT ANALYSIS prediction/detection of what the user is writing in a very UI interactive app (BLAZOR based) in the client side and running an ML.NET model (Sentiment analysis based on binary-classification) in the server side. From ML.NET perspective, the goal is to optimize the ML.NET model executions in the server by sharing the ML.NET objects used for predictions across Http requests and being able to implement very simple code to be used by the user when predicting, like the following line of code that you could write on any ASP.NET Core controller's method or custom service class: The object predictionEnginePool will be injected in the controller's constructor or into you custom class. Internally, it is optimized so the object dependencies are cached and shared across Http requests with minimum overhead when creating those objects. Blazor allows you to run C# code in the client side, as shown in the architecture figure. For this sample we chose to run the ML.NET model in the server side, so the model is protected within the service.

Model Builder is a simple UI tool for developers to build, train and ship custom machine learning models in their applications. Developers with no ML expertise can use this simple visual interface to connect to their data stored in files, SQL Server and more for training the model. Model Builder leverages best in class automated machine learning (AutoML) to evaluate different models. It produces the best model for your scenario without any tuning required from the developer. At the end, developers can generate code for training and consuming this model in their applications.