Streaming Feature Computation
A method for computing features in real time as events arrive. It relies on stream processing engines to perform continuous aggregation over unbounded event streams and must handle out-of-order events and state management.
Timeline (last 90 days)
Streaming feature computation is usually implemented on stream processing engines (such as Flink, Spark Structured Streaming or Kafka Streams), whose core is to define features as continuous aggregations over unbounded event streams.
In engineering terms, streaming feature computation needs to handle out-of-order events (via the watermark mechanism), state storage and recovery (checkpoint), and low-latency linkage between window results and online model inference.
All Facts (2)
Streaming feature computation is usually implemented on stream processing engines (such as Flink, Spark Structured Streaming or Kafka Streams), whose core is to define features as continuous aggregations over unbounded event streams.
50%UnverifiedIn engineering terms, streaming feature computation needs to handle out-of-order events (via the watermark mechanism), state storage and recovery (checkpoint), and low-latency linkage between window results and online model inference.
50%