How do you implement real-time AI inference integration with streaming data?

Real-time AI inference on streaming data requires low-latency processing pipelines. Architecture: data ingestion (Kafka, Kinesis, or Pulsar for event streams), stream processing (Apache Flink, Spark Streaming, or Kafka Streams for feature engineering), model serving (TorchServe, Triton, or TFServing for inference), and action dispatch (results route to downstream systems). Implementation: events flow from source systems to a message broker, stream processors enrich events with features (aggregations, lookups, transformations), enriched events are sent to the model serving endpoint for prediction, and results trigger automated actions or update dashboards. Feature engineering: maintain real-time feature stores (Feast, Tecton) that compute time-windowed aggregations (last 5 minutes, last hour), point-in-time lookups, and derived features. Latency optimization: colocate stream processor and model server, batch micro-batches of events for GPU efficiency, use model caching to avoid reloading, and implement circuit breakers for model unavailability. Use cases: real-time fraud detection (transaction events scored in under 50ms), recommendation engines (user activity triggers personalized suggestions), predictive maintenance (sensor data triggers anomaly alerts), and dynamic pricing (demand signals adjust prices). Monitoring: track end-to-end latency, event processing lag, prediction throughput, and data quality metrics (missing features, schema violations).