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总是在重复，尤其是错误！
文库
- 1 Inside Minerva: building scalable observability for eBay’s monetization platform
- 2 Meta’s Generative Ads Model (GEM): Scaling the Central Model and Compute Engine for Frontier Ads AI
- 3 大表治理之分表实践：带你手搓一个简单的数据库分表组件
- 4 CASTLE: training natural language search before the first real query
- 5 Reliable GPU compute for large training & inference jobs
- 6 Netflix Stratum Media Processing: Automated Container Right-Sizing
- 7 The Lifecycle of LLM-as-a-Judge: Building, Aligning, and Monitoring at scale
- 8 Running Apache Spark experiments in my sleep (and on a plane)
- 9 Netflix Conductor : The Next Chapter
- 10 Behind the Scenes: Evolving Netflix’s Ads Event Pipeline for Live — Part II
- 11 TimeSeries Tiered Storage Journey: Kafka/Flink Streams to Native Cassandra Cold Reads
- 12 Measuring the Impact of Personalized Recommendations
- 13 DataJunction as Netflix’s answer to the missing piece of the modern data stack
- 14 Integrating Netflix’s Foundation Model into Personalization applications
- 15 Agent 缺的不是记忆，是“教训”：causal-memory 的设计与实战
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