Uniting the Machine Learning and Data Streaming Ecosystems - Part 2

The text discusses the integration of machine learning (ML) and data streaming, emphasizing the role of SQL as a common language across programming ecosystems and highlighting the benefits and limitations of using SQL for streaming solutions. It explores how SQL facilitates the creation of directed acyclic graphs (DAGs) for declarative programming but notes its lack of composability for complex analytics. The text also describes various approaches to bridging the language gap between Python and Java ecosystems, such as using libraries like Faust for Python and Kafka Streams for Java, and highlights the challenges of integrating ML experiment management platforms with Kafka. It showcases examples of real-time ML applications, such as Centrica Hive's ML-powered microservices and Babylon's health risk prediction system. The text concludes by asserting the potential and readiness of the community to integrate ML and streaming solutions, inviting developers to share their experiences and solutions on community forums.

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