Towards A Methodological Framework for Production-ready AI-based Software Components

Markus Haug; Justus Bogner

doi:10.1145/3522664.3528616

Towards A Methodological Framework for Production-ready AI-based Software Components

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

Abstract

AI-based software systems have seen more widespread production deployment in recent years. However, many challenges currently complicate the integration of AI-based software components into production systems, and their operation and maintenance remains complex. In this research proposal, we summarize these challenges and propose a framework that enables the efficient design, integration, and operation of AI components.

Original language	English
Title of host publication	Proceedings - 1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	35-36
ISBN (Electronic)	9781450392754
DOIs	https://doi.org/10.1145/3522664.3528616
Publication status	Published - 2022
Externally published	Yes
Event	1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022 - Pittsburgh, United States Duration: 16 May 2022 → 17 May 2022

Conference

Conference	1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022
Country/Territory	United States
City	Pittsburgh
Period	16/05/22 → 17/05/22

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10.1145/3522664.3528616

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title = "Towards A Methodological Framework for Production-ready AI-based Software Components",

abstract = "AI-based software systems have seen more widespread production deployment in recent years. However, many challenges currently complicate the integration of AI-based software components into production systems, and their operation and maintenance remains complex. In this research proposal, we summarize these challenges and propose a framework that enables the efficient design, integration, and operation of AI components.",

author = "Markus Haug and Justus Bogner",

year = "2022",

doi = "10.1145/3522664.3528616",

language = "English",

pages = "35--36",

booktitle = "Proceedings - 1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022 ; Conference date: 16-05-2022 Through 17-05-2022",

}

Haug, M & Bogner, J 2022, Towards A Methodological Framework for Production-ready AI-based Software Components. in Proceedings - 1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022. Institute of Electrical and Electronics Engineers Inc., pp. 35-36, 1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022, Pittsburgh, United States, 16/05/22. https://doi.org/10.1145/3522664.3528616

Towards A Methodological Framework for Production-ready AI-based Software Components. / Haug, Markus; Bogner, Justus.
Proceedings - 1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 35-36.

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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AU - Haug, Markus

AU - Bogner, Justus

PY - 2022

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N2 - AI-based software systems have seen more widespread production deployment in recent years. However, many challenges currently complicate the integration of AI-based software components into production systems, and their operation and maintenance remains complex. In this research proposal, we summarize these challenges and propose a framework that enables the efficient design, integration, and operation of AI components.

AB - AI-based software systems have seen more widespread production deployment in recent years. However, many challenges currently complicate the integration of AI-based software components into production systems, and their operation and maintenance remains complex. In this research proposal, we summarize these challenges and propose a framework that enables the efficient design, integration, and operation of AI components.

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U2 - 10.1145/3522664.3528616

DO - 10.1145/3522664.3528616

M3 - Conference contribution

SP - 35

EP - 36

BT - Proceedings - 1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 16 May 2022 through 17 May 2022

ER -

Towards A Methodological Framework for Production-ready AI-based Software Components

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