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Technology readiness levels for machine learning systems.

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This study introduces the Machine Learning Technology Readiness Levels (M-TRL) framework, a systems engineering approach for developing robust and reliable artificial intelligence and machine learning systems. M-TRL ensures responsible AI development, mitigating risks associated with rushed processes.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning Systems Engineering
  • Spacecraft Systems Engineering

Background:

  • Modern machine learning (ML) development is often rushed, leading to technical debt, misaligned objectives, and system failures.
  • Traditional software engineering and spacecraft engineering employ rigorous processes for reliability and quality.
  • A gap exists in structured, diligent development processes for ML and artificial intelligence (AI).

Purpose of the Study:

  • To present a principled systems engineering approach for ML and AI development.
  • To introduce the Machine Learning Technology Readiness Levels (M-TRL) framework.
  • To ensure robust, reliable, and responsible ML/AI systems through a streamlined process.

Main Methods:

  • Developed the Machine Learning Technology Readiness Levels (M-TRL) framework.
  • Applied systems engineering principles to ML workflows.
  • Defined a common language (lingua franca) for cross-team collaboration in ML/AI.

Main Results:

  • The M-TRL framework provides a structured process for ML/AI development.
  • It ensures key distinctions from traditional software engineering are addressed.
  • The framework facilitates collaboration across diverse teams and organizations.

Conclusions:

  • The M-TRL framework enables the development of robust, reliable, and responsible ML/AI systems.
  • It mitigates risks associated with rapid ML development.
  • Use-cases demonstrate applicability across physics research, computer vision, and medical diagnostics.