Streaming Machine Learning with Tiered Storage

This post introduces the concept of streaming machine learning, where data is consumed directly from Kafka into ML frameworks like TensorFlow, eliminating the need for a separate data lake. It highlights the benefits of this approach for scalability, reliability, and reduced operational effort. It also discusses how Tiered Storage complements this by providing cost-effective long-term storage for Kafka data, enabling reprocessing for various use cases and simplifying ML infrastructure.