Computer Systems Have 99 Problems, Let's Not Make Machine Learning Another One
It identifies critical challenges for researchers and practitioners in computer systems using machine learning, but is incremental as it advocates for attention rather than presenting new solutions.
The paper addresses the integration of machine learning into computer systems, highlighting the need to tackle key issues such as security, system complexity, and reproducibility to realize its potential.
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.