Computer Systems Have 99 Problems, Let's Not Make Machine Learning Another One

TL;DR

This paper discusses the integration of machine learning into computer systems, emphasizing security, complexity, and reproducibility to ensure sustainable and effective deployment.

Contribution

It highlights key issues like security, complexity, and reproducibility in machine learning systems applied to computer systems, advocating for a renewed focus on these challenges.

Findings

Security concerns need to be addressed for ML in systems

Reproducibility is crucial for sustainable ML systems

System complexity impacts ML deployment and effectiveness

Abstract

Machine learning techniques are finding many applications in computer systems, including many tasks that require decision making: network optimization, quality of service assurance, and security. We believe machine learning systems are here to stay, and to materialize on their potential we advocate a fresh look at various key issues that need further attention, including security as a requirement and system complexity, and how machine learning systems affect them. We also discuss reproducibility as a key requirement for sustainable machine learning systems, and leads to pursuing it.