Streaming Machine Learning with Tiered Storage and Without a Data Lake

A scalable and reliable infrastructure for machine learning tasks can be built using the Apache Kafka ecosystem and Confluent Platform, simplifying the design of mission-critical real-time architectures. Streaming machine learning enables direct consumption of data streams from Confluent Platform into machine learning frameworks like TensorFlow, reducing the need for a traditional data lake. Tiered Storage in Confluent Platform combines local Kafka storage with remote storage layers, allowing data to be stored long-term without high costs or scalability issues. This simplifies model training and deployment, enabling rapid prototyping and data preprocessing, as well as robust and decoupled model management. A Kappa Architecture is a key pattern for building machine learning infrastructure, leveraging event streaming for processing both live and historical data. With this architecture, monitoring, testing, and analysis of the entire machine learning infrastructure can be critical but hard to realize in many architectures.

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