6 posts tagged “netflix”
2025
Netflix asks partners to consider the following guiding principles before leveraging GenAI in any creative workflow:
- The outputs do not replicate or substantially recreate identifiable characteristics of unowned or copyrighted material, or infringe any copyright-protected works
- The generative tools used do not store, reuse, or train on production data inputs or outputs.
- Where possible, generative tools are used in an enterprise-secured environment to safeguard inputs.
- Generated material is temporary and not part of the final deliverables.
- GenAI is not used to replace or generate new talent performances or union-covered work without consent.
[...] If you answer "no" or "unsure" to any of these principles, escalate to your Netflix contact for more guidance before proceeding, as written approval may be required.
— Netflix, Using Generative AI in Content Production
2018
A Netflix Web Performance Case Study (via) Fascinating description of how Netflix knocked the 3G loading times of their homepage in half for logged-out users by rendering the React templates on the server-side and using the bare amount of vanilla JavaScript necessary to get the homepage interactive—then XHR prefetching the full React code needed to power the subsequent signup flow. Via Alex Russell, who tweets “I’m increasingly optimistic that we can cap JS emissions by quarantining legacy frameworks to the server side.”
Among other things at Netflix the Mantis Query Language (MQL an SQL for streaming data) which ferries around approximately 2 trillion events every day for operational analysis (SPS alerting, quality of experience metrics, debugging production, etc) is written entirely in Clojure.
Beyond Interactive: Notebook Innovation at Netflix. Netflix have been investing heavily in their internal Jupyter notebooks infrastructure: it’s now the most popular tool for working with data at Netflix. They also use parameterized notebooks to make it easy to create templates for reusable operations, and scheduled notebooks for recurring tasks. “When a Spark or Presto job executes from the scheduler, the source code is injected into a newly-created notebook and executed. That notebook then becomes an immutable historical record, containing all related artifacts — including source code, parameters, runtime config, execution logs, error messages, and so on.”
Every day more than 1 trillion events are written into a streaming ingestion pipeline, which is processed and written to a 100PB cloud-native data warehouse. And every day, our users run more than 150,000 jobs against this data, spanning everything from reporting and analysis to machine learning and recommendation algorithms.
2010
The making of the NYT’s Netflix graphic. A database dump from Netflix, some clever hackery in ArcView GIS, hpricot to scrape Metacritic and a lot of careful thought about the UI for navigating the data.