ML.NET
E182435
ML.NET is an open-source, cross-platform machine learning framework for .NET developers to build and integrate custom ML models into .NET applications.
All labels observed (2)
How this entity was disambiguated
This entity first appeared as the object of triple T1621481 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
Target entity: ML.NET Context triple: [.NET Core, supportsFramework, ML.NET]
-
A.
Microsoft Cognitive Toolkit
Microsoft Cognitive Toolkit (CNTK) is an open-source deep learning framework developed by Microsoft for building, training, and deploying neural networks at scale.
-
B.
TensorFlow.js
TensorFlow.js is a JavaScript library that enables training and running machine learning models directly in the browser and in Node.js using TensorFlow.
-
C.
ML
ML is the postcode area in central Scotland that covers Motherwell and surrounding towns.
-
D.
ML
ML is a statically typed functional programming language developed at the University of Edinburgh, known for pioneering features like type inference, pattern matching, and modules that strongly influenced later languages such as Elm, Haskell, and OCaml.
-
E.
Swift for TensorFlow
Swift for TensorFlow is an experimental machine learning platform that integrates TensorFlow directly into the Swift programming language to enable differentiable programming and high-performance model development.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Target entity: ML.NET Target entity description: ML.NET is an open-source, cross-platform machine learning framework for .NET developers to build and integrate custom ML models into .NET applications.
-
A.
Microsoft Cognitive Toolkit
Microsoft Cognitive Toolkit (CNTK) is an open-source deep learning framework developed by Microsoft for building, training, and deploying neural networks at scale.
-
B.
TensorFlow.js
TensorFlow.js is a JavaScript library that enables training and running machine learning models directly in the browser and in Node.js using TensorFlow.
-
C.
ML
ML is the postcode area in central Scotland that covers Motherwell and surrounding towns.
-
D.
ML
ML is a statically typed functional programming language developed at the University of Edinburgh, known for pioneering features like type inference, pattern matching, and modules that strongly influenced later languages such as Elm, Haskell, and OCaml.
-
E.
Swift for TensorFlow
Swift for TensorFlow is an experimental machine learning platform that integrates TensorFlow directly into the Swift programming language to enable differentiable programming and high-performance model development.
- F. None of above. chosen
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
.NET library
ⓘ
machine learning framework ⓘ open-source software ⓘ |
| designGoal | enable custom ML models in .NET applications ⓘ |
| developer | Microsoft ⓘ |
| hasComponent |
DataView
ⓘ
Estimator ⓘ ML.NET self-linksurface differs ⓘ
surface form:
MLContext
PredictionEngine ⓘ Trainer ⓘ Transform ⓘ |
| integratesWith |
.NET 5+
ⓘ
.NET Core ⓘ ASP.NET Core Razor Pages ⓘ
surface form:
ASP.NET Core
Azure Machine Learning ⓘ |
| isCrossPlatform | true ⓘ |
| isOpenSource | true ⓘ |
| license | MIT License ⓘ |
| platform |
.NET Framework
ⓘ
surface form:
.NET
|
| programmingLanguage |
.NET Framework
ⓘ
surface form:
.NET
C# programming language ⓘ
surface form:
C#
|
| repository | https://github.com/dotnet/machinelearning ⓘ |
| supportsFeature |
AutoML
ⓘ
ONNX model consumption ⓘ data loading ⓘ data transformation ⓘ feature engineering ⓘ model deployment ⓘ model evaluation ⓘ model training ⓘ |
| supportsLanguage |
C# programming language
ⓘ
surface form:
C#
F# ⓘ Visual Basic .NET ⓘ
surface form:
VB.NET
|
| supportsModelFormat |
ONNX
ⓘ
TensorFlow models (via integration) ⓘ |
| supportsOperatingSystem |
Linux
ⓘ
Windows ⓘ macOS ⓘ |
| supportsScenario |
anomaly detection
ⓘ
binary classification ⓘ clustering ⓘ computer vision ⓘ multiclass classification ⓘ natural language processing ⓘ ranking ⓘ recommendation ⓘ regression ⓘ time series forecasting ⓘ |
| targetUser | .NET developers ⓘ |
| website | https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Subject: ML.NET Description of subject: ML.NET is an open-source, cross-platform machine learning framework for .NET developers to build and integrate custom ML models into .NET applications.
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.