Listen to Tim Berglund and guests unpack a variety of topics surrounding Apache Kafka®, Confluent, real-time data streaming, and the cloud.
TensorFlow is Google's version of wrangling various technologies to help group them together and work smoothly as large amounts of data flow through. Chris Mattmann, author of Machine Learning with TensorFlow, explains neural networks, how technology simulates cerebral processes that take place when our visual cortex receives a new image, and a use case that involves Apache Kafka® and event streaming to achieve TensorFlow's goals.
Kai Waehner defines machine learning in depth, describes the architecture of his dream machine learning pipeline, shares its relevance to Apache Kafka and the related ecosystem, and discusses the importance of security and fraud detection.
Tim Berglund and Michael Drogalis talk about all things stream processing: why it’s complex, how it has evolved, and what’s on the horizon to make it simpler.
Chris Riccomini tells us how Apache Kafka® and the stream processing framework Samza came about, and also what he’s doing these days at WePay—building systems that use Kafka as a primary datastore.
Gwen and Kai chat about machine learning architectures, and whether software engineers and data science can learn to get along.
Tim Berglund is a teacher, author, and technology leader with Confluent, where he serves as the senior director of developer advocacy. He can frequently be found at speaking at conferences in the U.S. and all over the world. Tim is the co-presenter of various O'Reilly training videos on topics ranging from Git to distributed systems, and he is the author of "Gradle Beyond the Basics." He lives in Littleton, CO, U.S., with the wife of his youth.
If there's something you want to know about Apache Kafka, Confluent or event streaming, please send us an email with your question and we'll hope to answer it on the next episode of Ask Confluent.
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