Towards Productizing AI/ML Models: An Industry Perspective from Data Scientists

TL;DR

This paper explores the challenges faced by data scientists and engineers in transitioning AI/ML models to production, highlighting issues with reproducibility, tooling, and best practices from an industry workshop perspective.

Contribution

It provides an industry perspective on the main barriers to productizing AI/ML models, emphasizing the need for improved support for software engineering and reproducibility.

Findings

Jupyter Notebook is the primary prototyping tool.

Lack of support for software engineering best practices.

Reproducibility remains a key challenge.

Abstract

The transition from AI/ML models to production-ready AI-based systems is a challenge for both data scientists and software engineers. In this paper, we report the results of a workshop conducted in a consulting company to understand how this transition is perceived by practitioners. Starting from the need for making AI experiments reproducible, the main themes that emerged are related to the use of the Jupyter Notebook as the primary prototyping tool, and the lack of support for software engineering best practices as well as data science specific functionalities.