# An Artificial Intelligence Life Cycle: From Conception to Production

arXiv: https://arxiv.org/abs/2108.13861 · HTML: https://mlsanity.com/paper/2108.13861/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2021.

## Built on by (most cited first, 11 of 11)

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- [Identifying Potential Inlets of Man in the Artificial Intelligence Development Process](https://mlsanity.com/paper/2403.14658/) (2024)
- [LoCoML: A Framework for Real-World ML Inference Pipelines](https://mlsanity.com/paper/2501.14165/) (2025)
- [Co-Producing AI: Toward an Augmented, Participatory Lifecycle](https://mlsanity.com/paper/2508.00138/) (2025)
- [Data and AI governance: Promoting equity, ethics, and fairness in large language models](https://mlsanity.com/paper/2508.03970/) (2025)
- [The Shrinking Lifespan of LLMs in Science](https://mlsanity.com/paper/2604.07530/) (2026)
- [Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery](https://mlsanity.com/paper/2303.11564/) (2023)
- [Building an Ethical and Trustworthy Biomedical AI Ecosystem for the Translational and Clinical Integration of Foundational Models](https://mlsanity.com/paper/2408.01431/) (2024)
- [Role and Use of Race in AI/ML Models Related to Health](https://mlsanity.com/paper/2504.00899/) (2025)

## Abstract (arXiv)

Drawing on our experience of more than a decade of AI in academic research, technology development, industry engagement, postgraduate teaching, doctoral supervision and organisational consultancy, we present the 'CDAC AI Life Cycle', a comprehensive life cycle for the design, development and deployment of Artificial Intelligence (AI) systems and solutions. It consists of three phases, Design, Develop and Deploy, and 17 constituent stages across the three phases from conception to production of any AI initiative. The 'Design' phase highlights the importance of contextualising a problem description by reviewing public domain and service-based literature on state-of-the-art AI applications, algorithms, pre-trained models and equally importantly ethics guidelines and frameworks, which then informs the data, or Big Data, acquisition and preparation. The 'Develop' phase is technique-oriented, as it transforms data and algorithms into AI models that are benchmarked, evaluated and explained. The 'Deploy' phase evaluates computational performance, which then apprises pipelines for model operationalisation, culminating in the hyperautomation of a process or system as a complete AI solution, that is continuously monitored and evaluated to inform the next iteration of the life cycle. An ontological mapping of AI algorithms to applications, followed by an organisational context for the AI life cycle are further contributions of this article.