COMPARATIVE ANALYSIS OF MODERN MACHINE LEARNING LIBRARIES AND FRAMEWORKS

E. Anikeev; Andrey Akimenko

doi:doi:10.58168/MIST2026_646-650

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COMPARATIVE ANALYSIS OF MODERN MACHINE LEARNING LIBRARIES AND FRAMEWORKS

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COMPARATIVE ANALYSIS OF MODERN MACHINE LEARNING LIBRARIES AND FRAMEWORKS

Section: SECTION 2. COMPUTER-AIDED DESIGN SYSTEMS

Proceedings: MODELING INFORMATION SYSTEMS AND TECHNOLOGIES – 2026 : PROCEEDINGS OF THE INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE DEDICATED TO THE 5TH ANNIVERSARY OF THE FACULTY OF COMPUTER SCIENCE AND TECHNOLOGY, VORONEZH, MARTH 30, 2026

UDC 004.89

E. Anikeev ¹

Andrey Akimenko ²

Author and publication information

Authors:

1. Voronezh State University of Forestry and Technologies named after G.F. Morozov
Russian Federation

2. Voronezh State University of Forestry and Technologies named after G.F. Morozov (department of Computer Technology and Microelectronic Engineering, docent)
from 01.01.1921 until now Russian Federation

Type:

Сonference article

DOI:

https://doi.org/10.58168/MIST2026_646-650

Pages:

from 646 to 650

Published:

30.06.2026

Subject area:

UDC 004.89

Language:

Russian

Keywords:

machine learning, frameworks, libraries, TensorFlow, PyTorch, JAX, Keras, MXNet, comparative analysis, artificial intelligence

Abstract and keywords

Abstract:
The article presents a comparative analysis of the most popular machine learning libraries and frameworks used in modern research and industrial projects. The key characteristics of TensorFlow, PyTorch, Keras, JAX, and MXNet, their architectural features, target applications, and model deployment tools are considered. Special attention is paid to the choice of framework depending on the specifics of the tasks – from research prototyping to industrial operation. The analysis of auxiliary machine learning ecosystem tools, including ONNX, PyTorch Lightning, and Fast.ai, is carried out.

Keywords:
machine learning, frameworks, libraries, TensorFlow, PyTorch, JAX, Keras, MXNet, comparative analysis, artificial intelligence

References

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4. Sanyadanam, A. Comparison of distributed Machine Learning frameworks in a fog environment: Conceptual and Performance analysis / A. Sanyadanam, S.N. Srirama // Internet of Things. – 2025. – Vol. 34. – Art. 101774. – DOI:https://doi.org/10.1016/j.iot.2025.101774. – URL: https://www.sciencedirect.com/science/article/abs/pii/S2542660525002884 (data obrascheniya: 19.02.2026).

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