Technology Readiness Levels for AI & ML

TL;DR

This paper introduces TRL4ML, a framework adapting systems engineering principles to AI/ML development, aiming to improve robustness, reliability, and collaboration across organizations.

Contribution

It proposes a structured TRL-based process specifically tailored for AI/ML systems, bridging gaps between traditional engineering and ML development practices.

Findings

Defines a clear ML-specific TRL framework.

Highlights differences between ML and traditional software engineering.

Provides a common language for organizational collaboration.

Abstract

The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, where mission critical measures and robustness are ingrained in the development process. Drawing on experience in both spacecraft engineering and AI/ML (from research through product), we propose a proven systems engineering approach for machine learning development and deployment. Our Technology Readiness Levels for ML (TRL4ML) framework defines a principled process to ensure robust systems while being streamlined for ML research and product, including key distinctions from traditional software engineering. Even more, TRL4ML defines a common language for people across the organization to work collaboratively on ML technologies.