MLOps for evolvable AI intensive software systems

Abstract

DevOps practices are the de facto sandard when developing software. The increased adoption of machine learning (ML) to solve problems urges us to adapt all the current approaches to developing a new standard that can take full benefit from the new solution. In this work we propose a graphical representation for DevOps for ML-based applications, namely MLOps, and also outline open research challenges. The pipeline aims to get the best of both worlds by maintaining the simple and iconic pipeline of DevOps, yet improving it by adding new circular steps for ML incorporation. This aims to create an ML-based development subsystem that can be self-maintained, and is capable of evolving side-by-side with the software development.