Journey To 1000 Models: Scaling Instagram’s Recommendation System
tl;dr: “In this post, we explore how Instagram has successfully scaled its algorithm to include over 1000 ML models without sacrificing recommendation quality or reliability. We delve into the intricacies of managing such a vast array of models, each with its own performance characteristics and product goals.”featured in #618
Real World Recommendation System – Part 1
- Nikhil Garg tl;dr: “The goal of this publication is to start from the basics, explain nuances of all the moving layers, and describe this universal recommendation system architecture.”featured in #411
Real World Recommendation System - Part 1
- Nikhil Garg tl;dr: "FAANG and other top tech companies have independently converged on a common architecture for production grade recommendation systems." This architecture is domain / vertical agnostic and can power all sorts of applications — from e-commerce and feeds to search, notifications, etc... Nikhil starts from the basics, explains nuances and describes this universal architecture.featured in #310
Building A Recommendation Engine Inside Postgres With Python And Pandas
- Craig Kerstiens tl;dr: Craig guides us through his experimental recommendation engine - with "SciPy, NumPy and Pandas there is a lot of interesting potential here."featured in #200