  Orchestrating a high-quality data preparation program is essential for
successful machine learning (ML), but it is known to be time and effort
consuming. Despite the impressive capabilities of large language models like
ChatGPT in generating programs by interacting with users through natural
language prompts, there are still limitations. Specifically, a user must
provide specific prompts to iteratively guide ChatGPT in improving data
preparation programs, which requires a certain level of expertise in
programming, the dataset used and the ML task. Moreover, once a program has
been generated, it is non-trivial to revisit a previous version or make changes
to the program without starting the process over again. In this paper, we
present ChatPipe, a novel system designed to facilitate seamless interaction
between users and ChatGPT. ChatPipe provides users with effective
recommendation on next data preparation operations, and guides ChatGPT to
generate program for the operations. Also, ChatPipe enables users to easily
roll back to previous versions of the program, which facilitates more efficient
experimentation and testing. We have developed a web application for ChatPipe
and prepared several real-world ML tasks from Kaggle. These tasks can showcase
the capabilities of ChatPipe and enable VLDB attendees to easily experiment
with our novel features to rapidly orchestrate a high-quality data preparation
program.

Databases;Artificial Intelligence;Human-Computer Interaction;Machine Learning