# The 6-Ds of Creating AI-Enabled Systems

arXiv: https://arxiv.org/abs/2202.03172 · HTML: https://mlsanity.com/paper/2202.03172/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 4 Feb 2022.

## Builds on (most cited first, 3 of 3)

- [A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective](https://mlsanity.com/paper/1811.03402/) (2018)
- [Data Readiness Levels](https://mlsanity.com/paper/1705.02245/) (2017)
- [Data Science and Digital Systems: The 3Ds of Machine Learning Systems Design](https://mlsanity.com/paper/1903.11241/) (2019)

## Abstract (arXiv)

We are entering our tenth year of the current Artificial Intelligence (AI) spring, and, as with previous AI hype cycles, the threat of an AI winter looms. AI winters occurred because of ineffective approaches towards navigating the technology valley of death. The 6-D framework provides an end-to-end framework to successfully navigate this challenge. The 6-D framework starts with problem decomposition to identify potential AI solutions, and ends with considerations for deployment of AI-enabled systems. Each component of the 6-D framework and a precision medicine use case is described in this paper.