Serve ML.NET Models as HTTP APIs with minimal configuration

Serve ML.NET Models as HTTP APIs with minimal configuration

Learn how to serve machine learning ML.NET models from HTTP APIs using minimal configuration. Serve ML.NET Models as HTTP APIs with minimal configuration.

Introduction

One of the most difficult tasks of building machine learning applications is deploying them to production. The ML.NET team is exploring ways to simplify the process and would like to hear your feedback.

When it comes to deploying machine learning models as web services, the bare minimum you need is a single endpoint to handle making predictions. One way to do that in .NET is using a technique known as “route-to-code.” In this post, I’ll show how “route-to-code” can help you quickly build highly scalable machine learning web services in about 60 lines of code!

For more information on “route-to-code”, see the ASP.NET documentation.

machine learning dotnet ml dotnet

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