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Technology Readiness Levels for Machine Learning Systems
Alexander Lavin

Fri Jul 17 08:30 AM -- 08:40 AM (PDT) @ None
Event URL: https://slideslive.com/38931760/technology-readiness-levels-for-machine-learning-systems?ref=account-folder-55868-folders »

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.

Author Information

Alexander Lavin (Augustus Intelligence)

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