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This article explains how Flink enables end‑to‑end machine‑learning workflows through AI Flow, covering the background of Lambda architecture, AI task stages, the advantages of Flink, AI Flow components, AI Graph concepts, integration with Python and TensorFlow, and a real‑world advertising recommendation use case.
This article explains how Flink enables end‑to‑end AI workflows through the AI Flow platform, covering the Lambda architecture background, AI task pipeline stages, the reasons for choosing Flink, AI Flow’s graph model, core services, integration with ML pipelines, and real‑world advertising recommendation use cases.
This article reviews recent progress in Apache Flink's AI ecosystem, explaining how Flink unifies batch and stream processing for machine‑learning pipelines, introduces the Flink ML Pipeline and Alink library, describes the AI Flow framework for end‑to‑end ML workflows, and presents a novel mini‑batch streaming iteration mechanism to support both offline and online learning scenarios.