Assessing the Quality of Computational Notebooks for a Frictionless Transition from Exploration to Production

TL;DR

This paper investigates how to improve the quality of computational notebooks to facilitate a seamless transition from data science exploration to production deployment, proposing best practices and tools.

Contribution

It introduces guidelines and proof-of-concept tools to enhance collaboration and quality assessment of computational notebooks for production readiness.

Findings

Identified key best practices for notebook collaboration

Developed proof-of-concept tools for quality assessment

Proposed guidelines to bridge exploration and production phases

Abstract

The massive trend of integrating data-driven AI capabilities into traditional software systems is rising new intriguing challenges. One of such challenges is achieving a smooth transition from the explorative phase of Machine Learning projects - in which data scientists build prototypical models in the lab - to their production phase - in which software engineers translate prototypes into production-ready AI components. To narrow down the gap between these two phases, tools and practices adopted by data scientists might be improved by incorporating consolidated software engineering solutions. In particular, computational notebooks have a prominent role in determining the quality of data science prototypes. In my research project, I address this challenge by studying the best practices for collaboration with computational notebooks and proposing proof-of-concept tools to foster guidelines compliance.