Agility in Software 2.0 -- Notebook Interfaces and MLOps with Buttresses and Rebars

TL;DR

This paper discusses enhancing agility in Machine Learning development through improved notebook interfaces and reinforced MLOps practices, aiming to address challenges in trustworthy AI system engineering.

Contribution

It introduces solutions for seamless transition from notebooks to IDEs and proposes metaphorical reinforcement techniques in MLOps for better AI system quality assurance.

Findings

Support for easy notebook to IDE transitions implemented

Reinforced engineering concepts improve MLOps robustness

Enhanced continuous engineering promotes trustworthy AI systems

Abstract

Artificial intelligence through machine learning is increasingly used in the digital society. Solutions based on machine learning bring both great opportunities, thus coined "Software 2.0," but also great challenges for the engineering community to tackle. Due to the experimental approach used by data scientists when developing machine learning models, agility is an essential characteristic. In this keynote address, we discuss two contemporary development phenomena that are fundamental in machine learning development, i.e., notebook interfaces and MLOps. First, we present a solution that can remedy some of the intrinsic weaknesses of working in notebooks by supporting easy transitions to integrated development environments. Second, we propose reinforced engineering of AI systems by introducing metaphorical buttresses and rebars in the MLOps context. Machine learning-based solutions are dynamic in nature, and we argue that reinforced continuous engineering is required to quality assure the trustworthy AI systems of tomorrow.