# Technology Readiness Levels for AI & ML

arXiv: https://arxiv.org/abs/2006.12497 · HTML: https://mlsanity.com/paper/2006.12497/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2020.

## Built on by (most cited first, 30 of 30)

- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare](https://mlsanity.com/paper/2309.12325/) (2023)
- [A Review of Causality for Learning Algorithms in Medical Image Analysis](https://mlsanity.com/paper/2206.05498/) (2022)
- [The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practice](https://mlsanity.com/paper/2301.09001/) (2023)
- [Quality issues in Machine Learning Software Systems](https://mlsanity.com/paper/2208.08982/) (2022)
- [Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery](https://mlsanity.com/paper/2505.16477/) (2025)
- [Machine Learning for Fraud Detection in E-Commerce: A Research Agenda](https://mlsanity.com/paper/2107.01979/) (2021)
- [Artificial Intelligence in Industry 4.0: A Review of Integration Challenges for Industrial Systems](https://mlsanity.com/paper/2405.18580/) (2024)
- [Mapping the Landscape of Generative AI in Network Monitoring and Management](https://mlsanity.com/paper/2502.08576/) (2025)
- [Towards deployment-centric multimodal AI beyond vision and language](https://mlsanity.com/paper/2504.03603/) (2025)
- [Next-Gen Machine Learning Supported Diagnostic Systems for Spacecraft](https://mlsanity.com/paper/2106.05659/) (2021)
- [Learning to Generate Training Datasets for Robust Semantic Segmentation](https://mlsanity.com/paper/2308.02535/) (2023)
- [Talking About the Assumption in the Room](https://mlsanity.com/paper/2502.13268/) (2025)
- [Reducing Effects of Swath Gaps on Unsupervised Machine Learning Models for NASA MODIS Instruments](https://mlsanity.com/paper/2106.07113/) (2021)
- [Towards Dependable Autonomous Systems Based on Bayesian Deep Learning Components](https://mlsanity.com/paper/2301.05297/) (2023)
- [The Systems Engineering Approach in Times of Large Language Models](https://mlsanity.com/paper/2411.09050/) (2024)
- [Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health](https://mlsanity.com/paper/2112.12554/) (2021)
- [Quality Issues in Machine Learning Software Systems](https://mlsanity.com/paper/2306.15007/) (2023)
- [Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities](https://mlsanity.com/paper/2312.03812/) (2023)
- [A Formal Model for Artificial Intelligence Applications in Automation Systems](https://mlsanity.com/paper/2407.03183/) (2024)
- [The Engineer's Dilemma: A Review of Establishing a Legal Framework for Integrating Machine Learning in Construction by Navigating Precedents and Industry Expectations](https://mlsanity.com/paper/2507.08908/) (2025)
- [Ethics Readiness of Artificial Intelligence: A Practical Evaluation Method](https://mlsanity.com/paper/2512.09729/) (2025)
- [Industrial AI Robustness Card for Time Series Models](https://mlsanity.com/paper/2512.11868/) (2025)
- [Causal Transfer in Medical Image Analysis](https://mlsanity.com/paper/2603.24388/) (2026)
- [A Dataset for the Recognition of Historical and Handwritten Music Scores in Western Notation](https://mlsanity.com/paper/2605.18436/) (2026)
- [Courant: a State-Adaptive Perceiver-Based Neural Surrogate with Local Support and Interpretable Field Decomposition](https://mlsanity.com/paper/2605.25115/) (2026)
- [A Conceptual Framework for Enhancing Workforce Readiness for Smart Manufacturing in the AI Era](https://mlsanity.com/paper/2608.11540/) (2026)
- [RAIL: An Automatic Classifier of the Artificial Intelligence Readiness Level](https://mlsanity.com/paper/2608.13428/) (2026)
- [Reinforcement Learning-Based Production Scheduling in an Industry-Based Coating Scenario Using the Digital Model Playground](https://mlsanity.com/paper/2608.14122/) (2026)
- [Large language models in medical time series analysis](https://mlsanity.com/paper/2609.22262/) (2026)

## Abstract (arXiv)

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.