Please use this identifier to cite or link to this item:
https://dair.nps.edu/handle/123456789/4913
Title: | Acquiring Maintainable AI-Enabled Systems |
Authors: | MAJ Iain Cruickshank, MAJ Shane Kohtz |
Keywords: | Artificial Intelligence Machine Learning Product Support Strategy Life Cycle Sustainment Plan |
Issue Date: | 1-May-2023 |
Publisher: | Acquisition Research Program |
Citation: | APA |
Series/Report no.: | Acquisition Management;SYM-AM-23-146 |
Abstract: | The Army and other services are quickly entering into an age where many, if not all, acquisitions programs will need to contend with acquiring Artificial Intelligence (AI)-enabled systems. While there has been research on how to acquire the data or model for an AI-enabled systems, sustainment considerations have been overlooked. Given the importance of sustainment for any acquisition program of record – both in terms of cost and in terms of program effectiveness – it is imperative that the Army, and the rest of DOD, plan for AI-enabled system maintenance. To address this gap, this paper proposes a framework and practices that draw on best practices from industry, program maintenance, and Machine Learning Operations (MLOps) to integrate AI maintenance into a product support strategy and Life Cycle Sustainment Plan. The framework outlines necessary components for sustainable AI and considers varying levels of maintenance to reduce operation and sustainment costs. |
Description: | SYM Presentation |
URI: | https://dair.nps.edu/handle/123456789/4913 |
Appears in Collections: | Annual Acquisition Research Symposium Proceedings & Presentations |
Files in This Item:
File | Description | Size | Format | |
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SYM-AM-23-146.pdf | 542.58 kB | Adobe PDF | View/Open |
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