2025-11-09 –, Tutorial Track 3
While AI copilots like Cursor and Claude Code have recently revolutionized software engineering workflows, many data scientists have so far been let down by the promise of AI. In this session, we’ll explore the capabilities of the Sphinx copilot, a Jupyter-native tool built specifically for data scientists, and learn tips and tricks on how to best leverage it to accelerate analytical workflows.
Sphinx is a new AI copilot that has been built from the ground up for data scientists and Jupyter notebooks. In this session, we'll work with a mock data environment to learn how to use Sphinx to aid with the process of exploring, modeling, and processing data. We’ll be covering the following:
- [:00 - :10] introduction
- [:10 - :15] installation
- [:15 - :25] using Sphinx for code generation and modelling workflows
- [:25 - :55] using Sphinx with data warehouses (e.g. Snowflake)
- [:55 - 1:15] building visualization apps with Streamlit + Sphinx
- [1:15 - 1:30] freeplay and Q&A
We hope that data scientists, analysts, and researchers will come out of this session armed with the knowledge of how to leverage AI copilots like Sphinx to accelerate their workflows.
No previous knowledge expected
I'm a software engineer with an interest in using AI to build better systems for data processing. Previously, I worked on ML training and inference systems at MosaicML and Databricks. Now at Sphinx, I'm working on creating AI copilots for data scientists.
