A Lifecycle-Oriented Taxonomy of Open-Source Tools for Machine Learning

Machine learning (ML) offers various tools, frame-works and platforms for resolving complex problems in compu-tational science and engineering. Machine learning frameworks have emerged as the cornerstone of modern research and in-novation. It redefines how knowledge is produced, validated, and disseminated. Open-source machine learning frameworks are emerging as a promising way to solve the challenges of large datasets, real-time constraints and heterogeneous system components. This study provides an extensive overview of open source tools based on the ML lifecycle. These tools are evaluated based on their purpose and key features for each stage of lifecycle, assisting researchers and practitioners in making informed decisions according to their requirements. The key challenges are identified and future research directions are also outlined.