  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.
