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

arXiv: https://arxiv.org/abs/2103.10548 · HTML: https://mlsanity.com/paper/2103.10548/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 8 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Built on by (most cited first, 2 of 2)

- [Pynblint: a Static Analyzer for Python Jupyter Notebooks](https://mlsanity.com/paper/2205.11934/) (2022)
- [Assessing the Quality of Computational Notebooks for a Frictionless Transition from Exploration to Production](https://mlsanity.com/paper/2205.11941/) (2022)

## Abstract (arXiv)

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.