资讯与观察
AI 行业动态、工程实践、产品方法和社区故事。
Integrating Netflix’s Foundation Model into Personalization applications
Authors: Divya Gadde, Ko-Jen Hsiao, Dhaval Patel and Moumita Bhattacharya The motivation for building the Netflix Foundation Model for personalization was laid out in this previous blog post. The Netflix homepage is pow…
DataJunction as Netflix’s answer to the missing piece of the modern data stack
by Olek Gorajek, Samuel Redai, Yian Shang ### Intro Our Netflix journey from data to dashboards, particularly within the space of the experimentation platform, always presented a unique set of challenges. This blog post…
26 分钟 · Netflix TechBlog
Measuring the Impact of Personalized Recommendations
By Kevin Zielnicki, Guy Aridor, Aurélien Bibaut, Allen Tran, Winston Chou, and Nathan Kallus ### Introduction Personalization is everywhere on the internet. It shapes the products we discover, the stories we read, the m…
30 分钟 · Netflix TechBlogLATEST STORIES
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TimeSeries Tiered Storage Journey: Kafka/Flink Streams to Native Cassandra Cold Reads精选导读
By Oleksii Tkachuk, Rajiv Shringi, Chris Lohfink, Abe Ratnofsky, Kartik Sathyanarayanan ### Introduction In an earlier post, we introduced the TimeSeries Abstraction, our high-throughput, low-latency platform designed t…
Behind the Scenes: Evolving Netflix’s Ads Event Pipeline for Live — Part II精选导读
By Yogesh Nagarur ### Introduction In Part 1 of this series, we shared how Netflix built the Ads Event pipeline and approached it as a system design problem: replacing fragmented pipelines with a centralized collection,…
Netflix Conductor: The Next Chapter精选导读
### Netflix Conductor : The Next Chapter by Aravindan Ramkumar on behalf of the Conductor team Conductor is the workflow orchestration engine Netflix uses to stitch micro-services into reliable, observable business proc…
Running Apache Spark experiments in my sleep (and on a plane)精选导读
By Prashanth Gedde Narayanaswamy As one of Netflix’s largest and critical data pipelines, member-sessionizer consolidates tens of billions of hourly client events, encompassing user taps, playbacks, and engagement signa…
The Lifecycle of LLM-as-a-Judge: Building, Aligning, and Monitoring at scale精选导读
By Emma Yanyang Kong, JJ Tan, Ishan Gupta, Lars Olds, Claire Campbell, David Fagnan, Ratna Kavuri, Rohan Gosain, Veli Balin, Minsu Jang, Louis Garcia ### Introduction Any team generating text with an LLM at scale hits t…
Build your slide template精选导读
Building a slide template starts with three foundational choices — colours, fonts and layouts. Sumeet Gayathri Moghe covers the basics of color theory and how to build a reusable palette, the essentials of typography, i…
Meta’s Generative Ads Model (GEM): Scaling the Central Model and Compute Engine for Frontier Ads AI精选导读
Last year, we introduced GEM, the industry’s largest recommendation system (RecSys) foundation model. GEM established Meta’s first LLM-inspired paradigm for ads recommendation, driving major performance gains. Building …
Meta’s Generative Ads Model (GEM): Scaling the Central Model and Compute Engine for Frontier Ads AI精选导读
Last year, we introduced GEM, the industry’s largest recommendation system (RecSys) foundation model. GEM established Meta’s first LLM-inspired paradigm for ads recommendation, driving major performance gains. Building …
The Craft of Practitioner Voice精选导读
A couple of months ago Jim Highsmith explained how practitioners writing for each other forms a distinct style of writing. He now adds a section to the article with more detail on the craft of how we write in this style…