In Solution Explorer, right-click the DataModels directory, and then select Add > New Item. In Solution Explorer, right-click on your project and select Add > New Folder. Add a new class to your project:Ĭreate a directory named DataModels in your project to save your data models: You need to create some classes for your input data and predictions. Under Advanced, change the value of Copy to Output Directory to Copy if newer. In Solution Explorer, right-click the model zip file and select Properties.Copy your pre-built model to the MLModels directory.Select the OK button on the Preview Changes dialog and then select the I Accept button on the License Acceptance dialog if you agree with the license terms for the packages listed.Īdd model to ASP.NET Core Web API project

Choose '' as the Package source, select the Browse tab, search for, select that package in the list, and select the Install button. In Solution Explorer, right-click on your project and select Manage NuGet Packages. Select the OK button on the Preview Changes dialog and then select the I Accept button on the License Acceptance dialog if you agree with the license terms for the packages listed. Choose '' as the Package source, select the Browse tab, search for Microsoft.ML, select that package in the list, and select the Install button. In the window that displays the different types of ASP.NET Core Projects, select API and the select the OK button.Ĭreate a directory named MLModels in your project to save your pre-built machine learning model files: In the Name text box, type 'SentimentAnalysisWebAPI' and then select the OK button.

Then select the ASP.NET Core Web Application project template. In the New Project dialog, select the Visual C# node followed by the Web node. Select File > New > Project from the menu bar. Use the ML.NET Sentiment Analysis tutorial to build your own model or download this pre-trained sentiment analysis machine learning model

Visual Studio 2019 or later or Visual Studio 2017 version 15.6 or later with the '.NET Core cross-platform development' workload installed.

Serving a model over a web API enables predictions via standard HTTP methods. Learn how to serve a pre-trained ML.NET machine learning model on the web using an ASP.NET Core Web API.

Net web api .pdf