Auto-aggregated global tech articles · 1,186 posts
Authors: Ying Li, Arjun Rao, Shradha SehgalIntroductionRecommendations sit at the heart of the Netflix experience. Our current production models rely on thousands of hand‑crafted features over users, items, and interactions, along with specialized architectures for sequence modeling, feature interac
How Airbnb teams build trustworthy Generative AI products by treating evaluation as a first-class engineering discipline; not an afterthought.Nestled into the lush hillside, this stunning modern retreat features striking natural wood architecture, terraced balconies, and a serene landscape.By: Rohit
How we built a Transformer-based sequence model that encodes years of guest behavior to surface the right listings at the right time.By: Daochen Zha, Chun How Tan, Xin Liu, Bin Xu, Han Zhao, Xiaowei Liu, Jun Shi, Tracy Yu, Hui Gao, Huiji Gao, Liwei He, Michael Kinoti, Stephanie Moyerman, and Sanjeev
To understand what can influence win rates, we analyzed evidence packets from one million disputes over a 16-week period. Here’s what the data shows and what it means for how you mitigate disputes.
By AI Platform’s Model Runtime team and Inference teamIntroductionMost organizations consume LLMs through hosted APIs. Netflix went further — we run the full stack ourselves, from model deployment through inference, inside our existing production environment rather than a separate ML silo. Some of t
Moving fast should not mean breaking things for your users. While testing on localhost is a good start, you need a near identical replica of production to catch any bugs before your code goes live. That is where a staging environment comes in. Heroku pipelines handle the infrastructure layout for yo
The Rust team has published a new point release of Rust, 1.97.1. Rust is a programming language that is empowering everyone to build reliable and efficient software. If you have a previous version of Rust installed via rustup, getting Rust 1.97.1 is as easy as: rustup update stable If you don't have
Hierarchical Interest Representation is a research area for Meta Ads. We’re exploring an upstream representation layer over the universe of Ads entities – users, advertisers, products, services – learning unified embeddings that connect users’ inferred interests with the breadth of what advertisers
Training an LLM is the easy part. The hard part is designing experiments and evaluations that you can trust enough to know whether the new model is actually an improvement.By: Baharak SaberidokhtIntroductionShipping a production LLM system means iterating fast on improvements to something that is, b
By Parth Jain, Rakesh Sukumar, Yingwu Zhao, Renzo Sanchez-Silva & Nathan FisherA deep dive into the engineering challenges of building a real-time service dependency map at Netflix scale: from streaming architectures and distributed aggregation pipelines to time-travel queries and the methodolog