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and technologies. The project was very successful, and over the years grew in size and usage internally at
Microsoft. Somewhere around 2011 the library was renamed to TLC ("the learning code"). TLC is widely used within Microsoft and is currently in version 3.10. The ML.NET library is a descendant of TLC, with Microsoft-specific features removed. I've used both libraries and, in many ways, the ML.NET child has surpassed its parent.
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341:(Automated Machine Learning) capabilities. Build 1.3.1 introduced a preview of Deep Neural Network training using C# bindings for Tensorflow and a Database loader which enables model training on databases. The 1.4.0 preview added ML.NET scoring on ARM processors and Deep Neural Network training with GPU's for Windows and Linux.
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When there are several variables that contribute to the overall score, it is possible to see a breakdown of each variable and which features had the most impact on the final score. The official documentation demonstrates that the scoring metrics can be output for debugging purposes. During training
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Even though the ML.NET library is new, its origins go back many years. Shortly after the introduction of the
Microsoft .NET Framework in 2002, Microsoft Research began a project called TMSN ("text mining search and navigation") to enable software developers to include ML code in Microsoft products
385:
has been an area of debate for AI Ethicists in recent years. A major issue for
Machine Learning applications is the black box effect where end users and the developers of an application are unsure of how an algorithm came to a decision or whether the dataset contains bias. Build 0.8 included model
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Ahmed, Zeeshan; Amizadeh, Saeed; Bilenko, Mikhail; Carr, Rogan; Chin, Wei-Sheng; Dekel, Yael; Dupre, Xavier; Eksarevskiy, Vadim; Erhardt, Eric; Eseanu, Costin; Filipi, Senja; Finley, Tom; Goswami, Abhishek; Hoover, Monte; Inglis, Scott; Interlandi, Matteo; Katzenberger, Shon; Kazmi, Najeeb;
442:(ONNX) format. This establishes an opportunity to use models in different environments that don't use ML.NET. It would be possible to run these models in the client side of a browser using ONNX.js, a javascript client-side framework for deep learning models created in the Onnx format.
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creation, and learners to handle binary classification, multi-class classification, and regression tasks. Additional ML tasks like anomaly detection and recommendation systems have since been added, and other approaches like deep learning will be included in future versions.
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is enabled from the 0.5 release. Support for x86 & x64 applications was added to build 0.7 including enhanced recommendation capabilities with Matrix
Factorization. A full roadmap of planned features have been made available on the official GitHub repo.
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search and navigation) for use internally within
Microsoft products. It was later renamed to TLC (the learning code) around 2011. ML.NET was derived from the TLC library and has largely surpassed its parent says Dr. James McCaffrey, Microsoft Research.
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Krivosheev, Gleb; Luferenko, Pete; Matantsev, Ivan; Matusevych, Sergiy; Moradi, Shahab; Nazirov, Gani; Ormont, Justin; Oshri, Gal; Pagnoni, Artidoro; Parmar, Jignesh; Roy, Prabhat; et al. (2019-05-15). "Machine
Learning at Microsoft with ML.NET".
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Microsoft
Research announced the popular Infer.NET model-based machine learning framework used for research in academic institutions since 2008 has been released open source and is now part of the ML.NET framework. The Infer.NET framework utilises
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programming language is popular with Data
Scientists, so it has introduced NimbusML the experimental Python bindings for ML.NET. This enables users to train and use machine learning models in Python. It was made open source similar to Infer.NET.
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Developers can train a
Machine Learning Model or reuse an existing Model by a 3rd party and run it on any environment offline. This means developers do not need to have a background in Data Science to use the framework. Support for the
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Microsoft's paper on machine learning with ML.NET demonstrated it is capable of training sentiment analysis models using large datasets while achieving high accuracy. Its results showed 95% accuracy on Amazon's 9GB review dataset.
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explainability API's that had been used internally in
Microsoft. It added the capability to understand the feature importance of models with the addition of 'Overall Feature Importance' and 'Generalized Additive Models'.
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ML.NET brings model-based Machine Learning analytic and prediction capabilities to existing .NET developers. The framework is built upon .NET Core and .NET Standard inheriting the ability to run cross-platform on
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Along with the rollout of the ML.NET preview, Microsoft rolled out free AI tutorials and courses to help developers understand techniques needed to work with the framework.
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Over time, it will enable other ML tasks like anomaly detection, recommendation system, and other approaches like deep learning using the benefits of added libraries.
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which has the added advantage of interpretability. The Infer.NET namespace has since been changed to Microsoft.ML.Probabilistic consistent with ML.NET namespaces.
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which uses ML.NET AutoML to perform model training and pick the best algorithm for the data. The ML.NET Model Builder preview is an extension for
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model format was introduced from build 0.3 in ML.NET. The release included other notable enhancements such as Factorization Machines,
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Hands-On Machine Learning with ML.NET: Getting started with Microsoft ML.NET to implement popular machine learning algorithms in C#
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390:& debugging of a model, developers can preview and inspect live filtered data. This is possible using the
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Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
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models when used together with NimbusML. The preview release of ML.NET included transforms for
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587:"Open Source, Cross-Platform ML.NET Simplifies Machine Learning -- Visual Studio Magazine"
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983:"Artificial Intelligence Can Reinforce Bias, Cloud Giants Announce Tools For AI Fairness"
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526:"Introducing ML.NET: Cross-platform, Proven and Open Source Machine Learning Framework"
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1116:"Microsoft open sources Infer.NET, its popular model-based machine learning framework"
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ML.NET Revealed: Simple Tools for Applying Machine Learning to Your Applications
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that uses ML.NET CLI and ML.NET AutoML to output the best ML.NET Model using a
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1091:"Microsoft open-sources Infer.NET AI code just in time for the weekend"
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Hastie, Trevor J. (1 November 2017). "Generalized Additive Models".
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612:"Microsoft debuts ML.NET cross-platform machine learning framework"
325:, Ensembles, LightLDA transform and OVA. The ML.NET integration of
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687:"Microsoft Launches ML.NET Open Source Machine Learning Framework"
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The first stable 1.0 release of the framework was announced at
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1141:"ML.NET – Export Machine Learning.Net models to ONNX format"
740:"ML.NET: The Machine Learning Framework for .NET Developers"
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2019. It included the addition of a Model Builder tool and
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ML.NET allows users to export trained models to the
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1066:"Announcing ML.NET 0.8 – Machine Learning for .NET"
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796:"Announcing ML.NET 0.7 (Machine Learning .NET)"
108:3.0.0-preview.23511.1 / 14 October 2023
506:List of numerical libraries for .NET framework
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1281:Esposito, Dino; Esposito, Francesco (2022).
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744:MSDN Magazine Connect() Special Issue 2018
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2270:Data mining and machine learning software
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550:"ML.NET: Machine Learning made for .NET"
266:programming languages. It also supports
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1008:"What it means to open AI's black box"
969:"dotnet/machinelearning-modelbuilder"
7:
2220:Microsoft Open Specification Promise
1929:Text Template Transformation Toolkit
1350:free and open-source software (FOSS)
468:Free and open-source software portal
2315:Open-source artificial intelligence
2300:Free software programmed in C Sharp
876:. SciSharp STACK. 21 February 2020.
501:List of numerical analysis software
971:. .NET Platform. 17 February 2020.
714:"Machine learning tasks in ML.NET"
574:at master · DotNet/MachineLearning
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446:AI School Machine Learning Course
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2139:TPM 2.0 Reference Implementation
1974:Windows Communication Foundation
1749:Microsoft Automatic Graph Layout
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425:Microsoft acknowledged that the
86:3.0.0 / 28 November 2023
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2305:Free software programmed in C++
2230:Open Source Security Foundation
2001:Windows Presentation Foundation
1744:Managed Extensibility Framework
768:"Release Microsoft ML.NET v0.3"
434:Machine learning in the browser
2320:Software using the MIT license
1884:Python Tools for Visual Studio
1844:Open Management Infrastructure
1754:Microsoft C++ Standard Library
738:James McCaffrey (2018-12-19).
610:Kareem Anderson (2017-05-09).
313:Open Neural Network Exchange (
1:
685:Tiwari, Aditya (2018-05-08).
2171:Microsoft Reciprocal License
1849:Open Neural Network Exchange
1774:Microsoft Enterprise Library
635:Ankit Asthana (2018-08-07).
524:Ankit Asthana (2017-05-07).
440:Open Neural Network Exchange
335:Build (developer conference)
1949:Virtual File System for Git
1824:Neural Network Intelligence
1759:Microsoft Cognitive Toolkit
1262:Mukherjee, Sudipta (2020).
496:Microsoft Cognitive Toolkit
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1243:Capellman, Jarred (2020).
662:"ML.NET 0.1 Release Notes"
585:David Ramel (2018-05-08).
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2290:Free statistical software
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2099:Extensible Storage Engine
1979:Windows Driver Frameworks
1889:R Tools for Visual Studio
1764:Microsoft Design Language
1364:Microsoft and open source
874:"SciSharp/TensorFlow.NET"
411:probabilistic programming
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2225:Open Letter to Hobbyists
2166:Microsoft Public License
2064:(v1.25, v2.0 & v4.0)
2006:Windows Template Library
1704:Dynamic Language Runtime
1369:Shared Source Initiative
660:Gal Oshri (2018-05-06).
641:blogs.msdn.microsoft.com
530:blogs.msdn.microsoft.com
16:Machine learning library
2310:Microsoft free software
1442:Windows Package Manager
1042:10.1201/9780203738535-7
1034:Statistical Models in S
937:10.1145/3292500.3330667
888:"ML.NET 1.4.0-preview2"
849:"Announcing ML.NET 1.0"
637:"Announcing ML.NET 0.4"
421:NimbusML Python support
110:; 10 months ago
2285:Probabilistic software
2275:Deep learning software
2215:F# Software Foundation
1216:"Infer.NET User Guide"
921:. pp. 2448–2458.
919:Microsoft & Yandex
591:Visual Studio Magazine
360:Command-line interface
88:; 9 months ago
2235:Outercurve Foundation
1789:Mixed Reality Toolkit
63:; 6 years ago
2280:Probabilistic models
1724:Fluent Design System
1604:.NET Micro Framework
1247:. Packt Publishing.
1036:. pp. 249–307.
821:"The ML.NET Roadmap"
415:probabilistic models
374:Model explainability
358:The ML.NET CLI is a
1447:WorldWide Telescope
1422:Microsoft PowerToys
1285:. Microsoft Press.
272:feature engineering
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2011:Windows UI Library
1924:T2 Temporal Prover
1894:RecursiveExtractor
1556:Small Basic Online
1432:Windows Calculator
1283:Programming ML.NET
35:Original author(s)
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2046:Z3 Theorem Prover
1854:Open Service Mesh
1769:Microsoft Detours
1639:ASP.NET Web Forms
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