A Holistic Framework for Enhancing Machine Learning Reliability in Computer Systems Engineering: Hardware-Software Integration

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Machine Learning development over the decades has evolved substantially. Advanced Machine Learning models are designed to help humans with real-world problems such as making predictions and developing autonomous vehicles. Nevertheless, it still grapples with various reliability issues such as being vulnerable to hardware faults and software errors, limiting its capability in computer systems engineering. This paper discusses a holistic analysis of the study which explored the reliability of Machine Learning systems in computer engineering through the integration of Hardware and Software systems.