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When a new idea is a big one, it causes massive effects on the way we build systems and the kinds of problems we can solve. Relational databases helped liberate information retrieval from the mainframe. The web made every computer user on earth a potential client of a single system. Machine Learning has exposed us to broad vistas of new recognition problems that we have begun to solve in impressive ways.
When big ideas meet and reinforce one another constructively, we see even larger effects. Event streaming has already caused a quiet revolution in the traditional ETL space, and is helping microservices architectures deliver on their yet-elusive promises of decoupling and evolvability. When event streaming and machine learning meet, the well-known problems of production ML systems find the same kind of solutions. Machine learning and event streaming can deliver all kinds of value to the world separately, but the ML community stands to grow by adopting real-time retraining using platforms like Apache Kafka.
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Keynote: The Happy Confluence of Streaming and Machine Learning
Tim is a teacher, author, and technology leader with Confluent, where he serves as the Senior Director of Developer Experience.