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文库
- 1 3个月用Harness重写90万行旧代码：踩过的坑都在这
- 2 Inside Minerva: building scalable observability for eBay’s monetization platform
- 3 Meta’s Generative Ads Model (GEM): Scaling the Central Model and Compute Engine for Frontier Ads AI
- 4 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
- 5 CASTLE: training natural language search before the first real query
- 6 Reliable GPU compute for large training & inference jobs
- 7 Netflix Stratum Media Processing: Automated Container Right-Sizing
- 8 The Lifecycle of LLM-as-a-Judge: Building, Aligning, and Monitoring at scale
- 9 Running Apache Spark experiments in my sleep (and on a plane)
- 10 Netflix Conductor : The Next Chapter
- 11 Behind the Scenes: Evolving Netflix’s Ads Event Pipeline for Live — Part II
- 12 TimeSeries Tiered Storage Journey: Kafka/Flink Streams to Native Cassandra Cold Reads
- 13 Measuring the Impact of Personalized Recommendations
- 14 DataJunction as Netflix’s answer to the missing piece of the modern data stack
- 15 Integrating Netflix’s Foundation Model into Personalization applications
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