AI and Data Streaming: Essential Resources for Developers

AI and machine learning applications depend on reliable, high-quality real-time and historical data, and data streaming can support their deployment by enabling continuous model training, persistent synchronization between source systems and ML platforms, real-time model inference, and high-volume processing. The material highlights the growing production use of AI and presents a collection of learning resources on the convergence of machine learning and streaming, including discussions of technical and organizational barriers, real-time generative AI with GPT-4, retrieval-augmented generation, abstractions for real-time ML platforms, migrations from batch to streaming ML, and online learning with tools such as Kafka, River, and Bytewax. It also directs readers to recorded sessions from Confluent’s Current 2023 event and additional GenAI resources.

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