In this workshop, we will build an end-to-end ML system to predict air quality that includes a feature pipeline to scrape new data and provide historical data (air quality observations and weather forecasts), a training pipeline to produce a model using the air quality observations and features, and a batch inference pipeline that updates a UI for Seattle. The system will be hosted on free serverless services - Modal, Hugging Face Spaces, and Hopsworks. It will be a continually improving ML system that keeps collecting more data, making better predictions, and provides a hindcast with insights into its historical performance.

Agenda will be available soon.

Register here