Comparison of machine learning software

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The following tables are a comparison of machine learning software such as software frameworks, libraries, and computer programs used for machine learning.

Machine learning software

Comparison of machine learning software[1][2][3]
Software Creator Initial release Script error: No such module "Vertical header". Platform Written in Interface Script error: No such module "Vertical header". Script error: No such module "Vertical header". Script error: No such module "Vertical header". Script error: No such module "Vertical header". Script error: No such module "Vertical header". Script error: No such module "Vertical header".
Apache Mahout Apache Software Foundation 2009 Yes Cross-platform Java, Scala Java, Scala, command line Yes Yes No No No Yes
Apache SINGA Apache Software Foundation 2015 Yes Linux, macOS, Windows C++, Python C++, Python Yes No Yes No No Yes
Apache SystemDS Apache Software Foundation 2015 Yes Linux, macOS, Windows Java, Python, C Java, Python, DML[4] Yes Yes Yes No No Yes
CatBoost Yandex 2017 Yes Linux, macOS, Windows C++, Python C++, Python, R, Java Yes No No No No Yes
Dlib Davis E. King 2002 Yes Cross-platform C++ C++, Python Yes No Yes No Yes Yes
ELKI ELKI project 2010 Yes Cross-platform Java Java Yes Yes No No No Yes
fastText Meta AI 2015 Yes Linux, macOS, Windows C++, Python Command line, Python Yes Yes No No Yes Yes
Flux FluxML contributors[5] 2018 Yes Linux, macOS, Windows Julia Julia Yes Yes Yes Yes Yes Yes
Gensim Radim Řehůřek 2009 Yes Linux, macOS, Windows Python, Cython Python No Yes No No Yes Yes
H2O H2O.ai 2011 Yes Cross-platform Java, Python, R Web interface, Python, R, Java Yes Yes Yes No Yes Yes
Infer.NET Microsoft Research 2008 Yes Windows, macOS, Linux C# C# Yes Yes No No No No
JAX Google 2018 Yes Linux, macOS, Windows Python Python Yes Yes Yes Yes No Yes
Jubatus Preferred Infrastructure and NTT 2011 Yes Linux C++ C++, Python, Java Yes Yes No No No No
KNIME KNIME AG 2006 Yes Cross-platform Java GUI, Python, R Yes Yes Yes No No Yes
LIBSVM Chih-Chung Chang and Chih-Jen Lin 2001 Yes Cross-platform C++, Java C++, Java, command line Yes No No No No Yes
LightGBM Microsoft 2016 Yes Linux, macOS, Windows C++, Python C++, Python, R Yes Yes No No No Yes
MATLAB[6] MathWorks 1984 No Linux, macOS, Windows C, C++, Java, MATLAB MATLAB Yes Yes Yes Yes Yes Yes
Microsoft Cognitive Toolkit Microsoft Research 2016 Yes Linux, Windows C++ C++, Python, C# Yes No Yes No No No
MindSpore Huawei 2020 Yes Linux, Windows, macOS, Android C++, Rust, Julia, Python, Java Python, C++, Java Yes No Yes Yes Yes Yes
ML.NET Microsoft 2018 Yes Linux, macOS, Windows C#, C++ C#, F# Yes Yes Yes No No Yes
mlpack mlpack developers 2008 Yes Cross-platform C++ C++, command line Yes Yes No No No Yes
MXNet Apache Software Foundation 2015 Yes Linux, macOS, Windows, Android, iOS C++, Python C++, Python, Julia, MATLAB, JavaScript, Go, R, Scala, Perl, Clojure Yes Yes Yes Yes Yes No
Orange University of Ljubljana 1996 Yes Cross-platform Python, C++ GUI, Python Yes Yes Yes No No Yes
RapidMiner RapidMiner 2006 No Cross-platform Java GUI Yes Yes Yes No No Yes
Scikit-learn scikit-learn developers 2007 Yes Cross-platform Python, Cython Python Yes Yes No No No Yes
Shogun Shogun Toolbox community 1999 Yes Cross-platform C++ C++, Python, Java, R, Ruby, C#, Lua, Octave Yes Yes No No No Yes
Spark MLlib Apache Software Foundation 2014 Yes Cross-platform Scala, Java Scala, Java, Python, R Yes Yes Yes No No Yes
TensorFlow Google Brain 2015 Yes Linux, macOS, Windows, Android C++, Python, CUDA Python, C++, Java, JavaScript, R Yes Yes Yes Yes Yes Yes
Theano University of Montreal 2007 Yes Linux, macOS, Windows Python, CUDA Python No No Yes Yes No No
Vowpal Wabbit Yahoo! Research and Microsoft Research 2007 Yes Linux, macOS, Windows C++ C++, command line, Python Yes No No No No Yes
Weka University of Waikato 1993 Yes Cross-platform Java GUI, Java, command line Yes Yes No No No Yes
XGBoost Tianqi Chen and contributors 2014 Yes Linux, macOS, Windows C++, Python C++, Python, R, Java, Julia Yes Yes No No No Yes

Other comparisons

Software Type Primary use GUI
Apache Mahout Library Distributed machine learning No
Apache SINGA Library Distributed machine learning and deep learning No
Apache SystemDS Platform End-to-end machine learning and data science workflows No
CatBoost Library Gradient boosting and decision tree learning No
Dlib Library Machine learning and computer vision No
ELKI Framework Data mining, clustering, outlier detection Yes
fastText Library Text classification, word embeddings, and natural language processing No
Flux Library Machine learning and deep learning No
Gensim Library Topic modeling, document retrieval, and similarity analysis No
H2O Platform Machine learning and AutoML Yes
Infer.NET Library Bayesian inference and probabilistic programming No
JAX Library Numerical computing, machine learning, and automatic differentiation No
Jubatus Framework Distributed online machine learning No
KNIME Platform Visual data analytics and machine learning workflows Yes
LIBSVM Library Support vector machine classification and regression No
LightGBM Library Gradient boosting and decision tree learning No
MATLAB Computing platform Numerical computing, statistics, and machine learning Yes
CNTK Framework Deep learning and machine learning No
MindSpore Framework machine learning and deep learning No
ML.NET Framework Machine learning for .NET Yes
mlpack Library General-purpose machine learning No
MXNet Framework Deep learning and scalable machine learning No
Orange Platform Visual programming, data mining, and machine learning Yes
RapidMiner Platform Data science and machine learning workflows Yes
Scikit-learn Library General-purpose machine learning No
Shogun Library General-purpose machine learning No
Spark MLlib Library Distributed machine learning on Apache Spark No
TensorFlow Framework Deep learning and machine learning Yes
Theano Library Machine learning and deep learning No
Vowpal Wabbit Library Online machine learning and reinforcement learning No
Weka Suite Machine learning and data mining Yes
XGBoost Library Gradient boosting No

Machine learning helper libraries and platforms

  • Apache OpenNLP — natural language processing toolkit
  • CUDA — GPU computing platform used to accelerate machine learning and deep learning workloads
  • Horovod — distributed training framework for deep learning
  • Hugging Face Transformers — library of pretrained transformer models built on other machine learning frameworks[7]
  • Kubeflow — machine learning platform for Kubernetes
  • Mallet — toolkit for natural language processing and text analysis
  • NumPy — numerical computing library used in machine learning[8][9]
  • OpenCV — computer vision library with machine learning functions
  • ONNX — open format for representing machine learning models
  • pandas — data analysis and data preparation library used in machine learning
  • PlaidML — tensor compiler and backend for machine learning frameworks
  • Polars — Dataframe library used for machine learning data preparation and analysis
  • PyArrow — columnar data library used in machine learning data processing
  • ROOT (TMVA) — data analysis framework with machine learning tools[10]
  • SciPy — scientific computing and optimization library used in machine learning[11]

Online development environments for machine learning

  • Google Colab — hosted Jupyter Notebook environment commonly used for machine learning and deep learning
  • JupyterLab — notebook-based development environment for machine learning and data science[12]
  • Jupyter Notebook — interactive notebook environment used for machine learning and data science[13][14]
  • Kaggle — online data science and machine learning platform[15]

See also

  • Yellowbrick — machine learning visualization library for model selection and diagnostics
  • ELI5 — library for explaining and debugging machine learning models
  • InterpretML — interpretable machine learning toolkit
  • SHAP — library for explaining machine learning model predictions

References

  1. ↑ Saucedo, Alejandro (2026-04-14), Awesome Production Machine Learning, https://github.com/EthicalML/awesome-production-machine-learning, retrieved 2026-04-14 
  2. ↑ Misiti, Joseph (2026-04-14), josephmisiti/awesome-machine-learning, https://github.com/josephmisiti/awesome-machine-learning, retrieved 2026-04-14 
  3. ↑ D, Søren A. (2026-04-11), sorend/awesome-python-machine-learning, https://github.com/sorend/awesome-python-machine-learning, retrieved 2026-04-14 
  4. ↑ "DML Language Reference - SystemDS 3.4.0-SNAPSHOT". https://apache.github.io/systemds/site/dml-language-reference. 
  5. ↑ Innes, Mike (2018-05-03). "Flux: Elegant machine learning with Julia". Journal of Open Source Software 3 (25): 602. doi:10.21105/joss.00602. Bibcode: 2018JOSS....3..602I. 
  6. ↑ "Machine Learning in MATLAB - MATLAB & Simulink". https://www.mathworks.com/help/stats/machine-learning-in-matlab.html. 
  7. ↑ Wolf, Thomas (2020). "Transformers: State-of-the-Art Natural Language Processing". Association for Computational Linguistics. pp. 38–45. doi:10.18653/v1/2020.emnlp-demos.6. https://aclanthology.org/2020.emnlp-demos.6/. Retrieved 2026-04-14. 
  8. ↑ Bourgin, David (2026-04-14), ddbourgin/numpy-ml, https://github.com/ddbourgin/numpy-ml, retrieved 2026-04-14 
  9. ↑ Mohandas, Goku. "NumPy for Machine Learning - Made With ML by Anyscale" (in en). https://madewithml.com/courses/foundations/numpy/. 
  10. ↑ "Machine learning with ROOT". CERN. https://root.cern/manual/tmva/. 
  11. ↑ "Compute options and notebook editor". IBM. https://www.ibm.com/docs/en/ws-and-kc?topic=tools-compute-options-notebook-editor. 
  12. ↑ "Using JupyterLab with ML Runtimes". https://docs.cloudera.com/cdsw/1.10.5/runtimes/topics/ml-runtimes-jupyter.html. 
  13. ↑ Verma, Ishu (2021-05-21). "Introduction to machine learning with Jupyter notebooks" (in en). https://developers.redhat.com/articles/2021/05/21/introduction-machine-learning-jupyter-notebooks. 
  14. ↑ "Using Jupyter Notebook for Machine Learning Development on NAS Systems - HECC Knowledge Base". https://www.nas.nasa.gov/hecc/support/kb/using-jupyter-notebook-for-machine-learning-development-on-nas-systems_576.html. 
  15. ↑ "What is Kaggle?" (in en). 2024-08-14. https://www.geeksforgeeks.org/machine-learning/what-is-kaggle/. 



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