Using Apache Flink for Model Inference: A Guide for Real-Time AI Applications | Confluent

This post details the implementation of remote model inference with Apache Flink on Confluent Cloud. It introduces the concept of 'model' as a first-class citizen in Flink SQL and Table APIs, enabling registration of remote AI models (e.g., Azure OpenAI, AWS SageMaker) via connection configurations and SQL DDL statements. It demonstrates the use of the `ML_PREDICT` function for tasks like generating vector embeddings for RAG. The post outlines key steps: data ingestion into Kafka, configuring API calls to model servers using Flink's asynchronous I/O, optimizing network efficiency through batching and efficient data encoding, and independent scaling and monitoring of model servers. It also provides best practices for error handling, model drift monitoring, and cloud resource optimization.