How to build a model annotation tool with FastAPI, Quarto & Shiny for Python

Name: How to build a model annotation tool with FastAPI, Quarto & Shiny for Python
Description: Gordon Shotwell, Senior Software Engineer at Posit walks through an end-to-end machine learning workflow with Posit Team. This demo will give you a robust pattern for hosting and sharing models on Connect before you deploy them to a customer-facing system.

Gordon Shotwell, Senior Software Engineer at Posit walks through an end-to-end machine learning workflow with Posit Team. This demo will give you a robust pattern for hosting and sharing models on Connect before you deploy them to a customer-facing system.

This will include:

How and why to use an API layer to serve and authenticate internal models
Why you should wrap your APIs in Python packages
Using Shiny for Python to build a data annotation app
Using Quarto to retrain models on a schedule

Timestamps:

1:27 - Quick overview of text classification model used in this example

2:15 - Overview of the people that will need to use the model (modellers, leadership, data team, annotators, other systems)

4:11 - Why APIs before UIs is a good rule

5:57 - What about Python packages?

8:23 - Advantages to using an API here

9:18 - Big picture overview of the workflow

11:17 - FastAPI on Posit Connect (Swagger interface)

15:55 - The way this model will be used (authorization by validating user)

19:00 - Building a delightful user experience by wrapping API in a package

25:07 - Quarto report for leadership team showing model statistics & deploying to Connect

26:34 - Retraining the model by scheduling Quarto doc on Connect

28:37 - Shiny for Python app for Annotators (people checking if model is producing correct results & helping improve the model)

35:28 - Overview / summary of this machine learning workflow