Data Science as a Service | Kumo AI Full Walkthrough

Building recommendation systems is hard. In data science, we can spend months wrangling data, training models, and still end up with mediocre results. That's where Kumo AI comes in — it's a service that abstracts away the complexity of building Graph Neural Networks (GNNs) for predictive analytics. In this guide, we'll build a complete e-commerce recommendation engine using real H&M data with 33 million transactions. By the end, we'll have a system that can: - Predict customer lifetime value for the next 30 days - Generate personalized product recommendations - Forecast purchase behavior to identify active customers All of this can be done in just a couple of hours - not months. 📌 Code: https://github.com/aurelio-labs/cookbook/blob/main/recsys/ecommerce/kumo-hm/kumo-hm.ipynb 💡 Kumo AI: https://bit.ly/4gduL04 📖 Article: https://www.jamesbriggs.io/kumo-ecommerce 👾 Discord: https://discord.gg/c5QtDB9RAP Twitter: https://twitter.com/jamescalam LinkedIn: https://www.linkedin.com/in/jamescalam/ #datascience #machinelearning #python 00:00 Kumo AI and GNNs 07:39 Kumo Setup 12:17 Kumo Connectors 14:45 Getting Data into BigQuery 20:39 Building the Graph in Kumo 28:34 Predictive Query Language (PQL) 35:01 Personalized Product Recommendations 38:44 Predicting Purchase Volume 41:44 Making Predictions with Kumo 27:10 Analysis and Prediction with Kumo 52:36 When to use Kumo