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Technology Readiness Levels for Machine Learning Systems

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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 engrained in the development process. Drawing on experience in both spacecraft engineering and AI/ML research through product, we have developed a proven systems engineering approach for machine learning development and deployment. Our “Technology Readiness Levels for Machine Learning” (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 lingua franca for people across teams and organizations to work collaboratively on AI/ML technologies. In this talk I elucidate TRL4ML with several real world use-cases developing ML algorithms and models from basic research through productization and deployment.

Keywords: Systems ML, Development and deployment, software engineering.

About the Speaker:

Alexander Lavin is a leading AI researcher and software engineer, specializing in probabilistic machine learning and human-centric AI systems. Lavin founded Latent Sciences, a startup commercializing his patented AI platform for predictive modeling neurodegenerative diseases, which was acquihired into a stealth enterprise AI company where he served as Chief Scientist. Previously he was a Senior Research Engineer at both Vicarious AI and Numenta, building artificial general intelligence for robotics, and developing biologically-derived ML algorithms, respectively. Lavin used to work in spacecraft systems, and he is now an AI Advisor for NASA . Lavin earned his Masters in Mechanical Engineering at Carnegie Mellon, a Masters in Engineering Management with Duke University, and Bachelors in Mech&Aero Engineering at Cornell University. He has won several awards for work in space robotics and rocket propulsion, published in top journals and conferences across AI/ML and neuroscience, and was a Forbes 30 Under 30 honoree in Science. In his free time, Lavin enjoys running, yoga, live music, and reading sci-fi and theoretical physics books.

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Part of ML@CL Seminar Series focusing on early career researchers in topics relevant to machine learning and statistics.

This talk is part of the ML@CL Seminar Series series.

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