请问HN：Kafka或事件驱动系统在大型语言模型基础设施中是如何使用的？
我很好奇一些事件驱动技术，比如Kafka（或其他替代方案）是如何融入大型大语言模型（LLM）提供商的后端和/或基础设施的。
我心中有一些问题，主要包括：
1. 大型LLM提供商是如何处理训练数据、评估结果和人类反馈的流动的？这些是通过事件流（如Kafka）进行实时处理，还是更依赖于批处理和传统的ETL流程？
2. 对于复杂的机器学习管道（例如数据摄取 -> 预处理 -> 训练 -> 评估 -> 部署），他们是使用事件驱动的编排，每个阶段发布一些完成事件，还是使用像Airflow这样的传统工作流编排工具，通过轮询管理依赖关系？
3. 他们是如何处理实时性能监控和安全信号的？这些是能够触发即时响应（如模型回滚）的事件驱动系统，还是主要是批量分析，反应有些延迟？
我基本上是在试图理解事件驱动范式在现代人工智能基础设施中的适用程度，如果有人在这方面工作过或正在工作，任何高层次的见解都将非常欢迎。
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Curious how some event-driven technologies like Kafka (or alternatives) fit into the backend and/or infrastructure of large LLM providers.<p>Some of the questions I have in mind are more:<p>1. How do large LLM providers handle the flow of training data, evaluation results and human feedback? Are these managed through event streams (like Kafka) for real-time processing or do they rely more on batch processing and traditional ETL pipelines?<p>2. For complex ML pipelines with deps (eg. data ingestion -> preprocessing -> training -> evaluation -> deployment), do they use event-driven orchestration where each stage publishes some completion events or do they use traditional workflow orchestrators like Airflow with polling-based dependency management?<p>3. How do they handle real-time performance monitoring and safety signals? Are these event-driven systems that can trigger immediate responses (like model rollbacks) or are they primarily batch analytics with some delayed reactions?<p>I'm basically trying to understand how far the event-driven paradigm fits in modern AI infra and I would love any high-level insights if someone is (or has been) working with it.