Streaming Machine Learning with Tiered Storage

Both approaches have their pros and cons. The blog post Machine Learning and Real-Time Analytics in Apache Kafka Applications and the Kafka Summit presentation Event-Driven Model Serving: Stream Processing vs. RPC with Kafka and TensorFlow discuss this in detail. There are more and more applications where the analytic model is directly embedded into the event streaming application, making it robust, decoupled, and optimized for performance and latency. The model can be loaded into the application when starting it up (e.g., using the TensorFlow Java API). Model management (including versioning) depends on your build pipeline and DevOps strategy. For example, new models can be embedded into a new Kubernetes pod which simply replaces the old pod. Another commonly used option is to send newly trained models (or just the updated weights or hyperparameters) as a Kafka message to a Kafka topic.