Towards Productizing AI/ML Models: An Industry Perspective from Data Scientists
This addresses the problem of AI/ML model deployment for data scientists and software engineers in industry, but it is incremental as it reports on existing perceptions without introducing new solutions.
The paper tackles the challenge of transitioning AI/ML models to production-ready systems by reporting results from a workshop in a consulting company, finding that practitioners face issues with reproducibility, reliance on Jupyter Notebooks, and lack of software engineering best practices.
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.