Technology Readiness Levels for AI & ML
This addresses the need for more reliable and collaborative ML development processes across organizations, though it is incremental by adapting existing engineering methods to ML.
The paper tackles the problem of rushed and undisciplined development in machine learning systems, which leads to technical debt and failures, by proposing a Technology Readiness Levels for ML (TRL4ML) framework based on systems engineering principles to ensure robust and streamlined processes.
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.