论文原文：MLSys: The New Frontier of Machine Learning Systems
这是一篇发表意义大于内容本身的一篇论文，它是一个全新的会议 MLSys 的开篇。所谓 MLSys，顾名思义就是 Machine Learning 和 System 的交叉领域。
在论文中，作者从两个维度阐述了 MLSys 涉及的核心问题： 维度一：
- How should software systems be designed to support the full machine learning lifecycle, from program- ming interfaces and data preprocessing to output interpretation, debugging and monitoring?
- How should hardware systems be designed for machine learning?
- How should machine learning systems be designed to satisfy metrics beyond predictive accuracy, such as power and memory efficiency, accessibility, cost, latency, privacy, security, fairness, and interpretability?
维度二：
- high-level systems for ML that support interfaces and workflows for ML development—the analogue of traditional work on programming languages and software engineering.
- low-level systems for ML that involve hardware or software—and that often blur the lines between the two—to support training and execution of models, the analogue of traditional work on compilers and architecture.
这篇论文最流弊的地方就是它的作者署名(亮点自寻)： 