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文库
- 1 Powered by AI: Instagram’s Explore recommender system
- 2 Accelerating Video Quality Control at Netflix with Pixel Error Detection
- 3 ML Observability: Bringing Transparency to Payments and Beyond
- 4 From Facts & Metrics to Media Machine Learning: Evolving the Data Engineering Function at Netflix
- 5 Unlocking Dynamic Pages: The Evolution of Netflix’s Client-Server GraphQL APIs
- 6 Behind the Streams: Building a Reliable Cloud Live Streaming Pipeline for Netflix. Part 2.
- 7 Empowering Netflix Engineers with Incident Management
- 8 Scaling Muse: How Netflix Powers Data-Driven Creative Insights at Trillion-Row Scale
- 9 Building a Resilient Data Platform with Write-Ahead Log at Netflix
- 10 100X Faster: How We Supercharged Netflix Maestro’s Workflow Engine
- 11 How and Why Netflix Built a Real-Time Distributed Graph: Part 1 — Ingesting and Processing Data Streams at Internet Scale
- 12 Behind the Streams: Real-Time Recommendations for Live Events Part 3
- 13 Post-Training Generative Recommenders with Advantage-Weighted Supervised Finetuning
- 14 Bridging Affordability, Familiarity, and Novelty: DoorDash’s LLM-assisted personalization framework
- 15 Using LLMs to infer grocery preferences from DoorDash restaurant orders
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