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ML.NET is an open-source machine-learning framework for building, training and deploying custom models in .NET applications. It includes AutoML and supports scenarios involving TensorFlow and ONNX.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Microsoft — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use ML.NET when you want to develop a machine-learning model as part of a .NET application, including through the tutorial’s Visual Studio Model Builder workflow or the ML.NET CLI.

Who it is for

  • Developers building .NET applications that need custom machine-learning features.
  • C# developers who want a visual model-building workflow in Visual Studio or a command-line workflow.

Documented uses

  • Train a classifier to identify positive or negative sentiment in customer reviews.
  • Build models for image classification, numeric-value prediction, time-series forecasting or recommendations.
  • Add custom prediction functionality to a .NET application.
Before you choose

Requirements & limits

The project page identifies ML.NET as open source. The getting-started tutorial lists Windows, Linux and macOS, with its specific Visual Studio workflow requiring the stated Visual Studio and .NET components.

  • The framework is designed for .NET applications; the tutorial uses a C# console app.
  • The tutorial documents multiple model scenarios, but its sentiment-classification example uses local training.
  • The Visual Studio tutorial combines a .NET 8 SDK prerequisite with a project-framework selection of .NET 10.0; the CLI instructions separately specify .NET 8.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Create a sentiment model with Visual Studio Model Builder

Set up a .NET console app and use Model Builder to start a customer-review sentiment model.

Before you start

  • Visual Studio 2026 with the .NET desktop development workload and ML.NET Model Builder component.
  • .NET 8 SDK, as specified in the tutorial prerequisites.
  • The Sentiment Labelled Sentences dataset from the UCI Machine Learning Repository.

Steps

  1. Create a C# Console App in Visual Studio and select .NET 10.0 as the framework, following the tutorial.

  2. Add an ML.NET Machine Learning Model item to the project and open it in Model Builder.

  3. Select Data classification and the local training environment for the sentiment task.

  4. Download and unzip the sentiment dataset, then use the tutorial’s data-preparation instructions to continue.

Expected outcome

A configured Model Builder workflow for training a review sentiment classifier; the cited tutorial continues with data selection and later model use.

Guide limitations

  • The tutorial notes that classification supports local training only in the described workflow.
  • The ML.NET CLI is in preview and its installation notes specify .NET 8 support; these CLI details are separate from the Visual Studio Model Builder steps.
Documentation 1 ↗
Evidence and transparency

Sources & review

dotnet.microsoft.com ↗Guide documentation ↗
Source record details
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Sources checked
12 October 2026
Original website title
ML.NET - machine learning made for .NET | .NET · Official homepage title · retained in its source language
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