Scalable AI.pdf (1.35 MB)

Scalable AI

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Scalability is a critical concept in many engineering disciplines and is crucial to realizing operational AI capabilities. We identify three areas of focus to advance scalable AI:

• Scalable management of data and models to overcome data scarcity and collection challenges, and promote reusing and recombining capabilities to scale across missions
• Enterprise scalability of AI development and deployment including establishing production pipelines, extensible system architectures, and modern policies and acquisition practices to maintain advanced capabilities and take advantage of rapid innovation in AI technologies
• Scalable algorithms and infrastructure to fully apply the power of AI to critical missions, including centralized data center capabilities and distributed cloud-enabled and network-enabled applications for edge devices

For each area, we identify ongoing work as well as challenges and opportunities in developing and deploying AI systems at scale.

Funding

Department of Defense Contract No. FA8702-15-D-0002

History

Publisher Statement

This material is based upon work funded and supported by the Department of Defense under Contract No. FA8702-15-D-0002 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center. The view, opinions, and/or findings contained in this material are those of the author(s)and should not be construed as an official Government position, policy, or decision, unless designated by other documentation. References herein to any specific commercial product, process, or service by trade name, trade mark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by Carnegie Mellon University or its Software Engineering Institute. NO WARRANTY. THIS CARNEGIE MELLON UNIVERSITY AND SOFTWARE ENGINEERING INSTITUTE MATERIAL IS FURNISHED ON AN “AS-IS” BASIS. CARNEGIE MELLON UNIVERSITY MAKES NO WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED, AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR PURPOSE OR MERCHANTABILITY, EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF THE MATERIAL. CARNEGIE MELLON UNIVERSITY DOES NOT MAKE ANY WARRANTY OF ANY KIND WITH RESPECT TO FREEDOM FROM PATENT, TRADEMARK, OR COPYRIGHT INFRINGEMENT

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©2021 Carnegie Mellon University This material has been approved for public release and unlimited distribution. Please see Copyright notice for non-US Government use and distribution. Internal use:* Permission to reproduce this material and to prepare derivative works from this material for internal use is granted, provided the copyright and “No Warranty” statements are included with all reproductions and derivative works. External use:* This material may be reproduced in its entirety, without modification, and freely distributed in written or electronic form without requesting formal permission. Permission is required for any other external and/or commercial use. Requests for permission should be directed to the Software Engineering Institute at permission@sei.cmu.edu. * These restrictions do not apply to U.S. government entities.

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