InfoQ AI ML Data Engineering:Article: Runtime-Agnostic AI 工作流: A Pattern for Production Durability and Fast Eval It…
原文摘要:AI 工作流 have two needs that trade off directly. Running reliably in production requires persisting and distributing every step so it survives crashes, deploys, and restarts. B 来源:InfoQ AI ML Data Engineering。建议继续查看原文,重点核对它影响的工具入口、成本、风险和真实使用场景。