Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems

TL;DR

This paper discusses the challenges in developing efficient, reliable, and secure ML systems and proposes an outline for an agile design methodology tailored to meet these complex requirements.

Contribution

It introduces a novel agile design methodology framework specifically aimed at creating ML systems that are efficient, reliable, and secure, addressing current infrastructure limitations.

Findings

Identifies key challenges in ML system development

Proposes an outline for an agile design methodology

Highlights the importance of user-defined constraints

Abstract

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.