LLMs have a lot of hype around them these days. Let’s demystify how they work and see how we can put them in context for data science use. As data scientists, we want to make sure our results are inspectable, reliable, reproducible, and replicable. We already have many tools to help us in this front. However, LLMs provide a new challenge; we may not always be given the same results back from a query. This means trying to work out areas where LLMs excel in, and use those behaviors in our data science artifacts. This talk will introduce you to LLMs, the Chatlas packages, and how they can be integrated into a Shiny to create an AI-powered dashboard (using querychat). We’ll see how we can leverage the tasks LLMs are good at to better our data science products.

Shiny is a framework for building web applications and data dashboards in Python. In this workshop, you will see how the basic building blocks of shiny can be extended to create your own scalable production-ready python applications.

In particular, this workshop covers:

• Overview of the basic building blocks of a Shiny for Python application
• How to refactor applications into shiny modules
• How to write tests for your shiny application
• Deploy and share your application

At the end of this course you will be able to:

• Build a Shiny app in Python
• Refactor your reactive logic into Shiny Modules
• Identify when to write Shiny modules
• Write unit tests and end-to-end tests for your shiny application
• Deploy and share your application (for free!)