Uniting Machine Learning and Data Streaming Technologies Part 2

What this post added

This post details the technical challenges and solutions for integrating Python's ML ecosystem with JVM-based data streaming platforms like Kafka and Flink. It explores the use of SQL as an intermediary, the mechanisms for inter-process communication (Py4J, gRPC, PemJa), and the role of Apache Arrow. It also discusses the current state of ML experiment management platform integrations with Kafka, noting the lack of direct managed integrations and the reliance on custom connectors or SDKs. Code examples are provided for building streaming ML solutions using the Confluent Python Client.