  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.
