DevOps for AI - Challenges in Development of AI-enabled Applications
Paper i proceeding, 2020

When developing software systems that contain Machine Learning (ML) based components, the development process become significantly more complex. The central part of the ML process is training iterations to find the best possible prediction model. Modern software development processes, such as DevOps, have widely been adopted and typically emphasise frequent development iterations and continuous delivery of software changes. Despite the ability of modern approaches in solving some of the problems faced when building ML-based software systems, there are no established procedures on how to combine them with processes in ML workflow in practice today. This paper points out the challenges in development of complex systems that include ML components, and discuss possible solutions driven by the combination of DevOps and ML workflow processes. Industrial cases are presented to illustrate these challenges and the possible solutions.

Agile software development

AI

Machine Learning

Författare

Lucy Lwakatare

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for Cyber Physical Systems

Ivica Crnkovic

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for Cyber Physical Systems

Jan Bosch

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for Testing, Requirements, Innovation and Psychology

2020 28th International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2020

9238323

28th International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2020
Split / Online, Croatia,

Ämneskategorier

Programvaruteknik

Systemvetenskap

Datorsystem

DOI

10.23919/SoftCOM50211.2020.9238323

Mer information

Senast uppdaterat

2021-06-15