The real-world use cases of Machine Learning (ML) have exploded over the past few years. However, the current computing infrastructure is insufficient to support all real-world applications and scenarios. Apart from high efficiency requirements, modern ML systems are expected to be highly reliable against hardware failures as well as secure against adversarial and IP stealing attacks. Privacy concerns are also becoming a first-order issue. This article summarizes the main challenges in agile development of efficient, reliable and secure ML systems, and then presents an outline of an agile design methodology to generate efficient, reliable and secure ML systems based on user-defined constraints and objectives.
翻译：过去几年来,机器学习(ML)的实际使用案例爆发,然而,目前的计算基础设施不足以支持所有实际应用和情景,除了效率高的要求外,现代ML系统预计对硬件故障高度可靠,对对抗性攻击和知识产权盗窃攻击也非常安全,隐私问题也正在成为一个头等问题,文章总结了灵活发展高效、可靠和安全的ML系统的主要挑战,然后概述了根据用户界定的限制和目标生成高效、可靠和安全的ML系统的灵活设计方法。
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