The Goldilocks Framework: Towards Selecting the Optimal Approach to Conducting AI Projects
Paper in proceeding, 2022

Artificial intelligence is increasingly becoming important to businesses since many companies have realized the benefits of applying Machine Learning (ML) and Deep Learning (DL) into their operations. Nevertheless, ML/DL technologies' industrial development and deployment examples are still rare and generally confined within a small cluster of large international companies who are struggling to apply ML more broadly and deploy their use cases at a large scale. Meanwhile, current AI market has started offering various solutions and services. Thus, organizations must understand how to acquire AI technology based on their business strategy and available resources. This paper discusses the industrial experience of developing and deploying ML/DL use cases to support organizations in their transformation towards AI. We identify how various factors, like cost, schedule, and intellectual property, can be affected by the choice of approach towards ML/DL project development and deployment within large international engineering corporations. As a research result, we present a framework that covers the trade-offs between those various factors and can support engineering companies to choose the best approach based on their long-term business strategies and, therefore, would help to accomplish their ML/DL project deployment successfully.

Artificial Intelligence

Deep Learning

Machine Learning

developing and deploying AI project

Engineering industry

Author

Rimman Dzhusupova

Control & Safety Systems McDermott

Jan Bosch

Chalmers, Computer Science and Engineering (Chalmers), Interaction Design and Software Engineering

Helena Holmström Olsson

Malmö university

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

124-135
9781450392754 (ISBN)

1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022
Pittsburgh, USA,

Subject Categories

Production Engineering, Human Work Science and Ergonomics

Other Mechanical Engineering

Business Administration

DOI

10.1145/3522664.3528595

More information

Latest update

7/14/2022