“Python + Keras + TensorFlow + DeepLearning4j + Apache Kafka + Kafka Streams” => New example added to my “Machine Learning + Kafka Streams Examples” Github project.
Machine Learning / Deep Learning models can be used in different ways to do predictions. Natively in the application or hosted in a remote model server. Then you combine stream processing with RPC / Request-Response paradigm. This blog post shows examples of stream processing vs. RPC model serving using Java, Apache Kafka, Kafka Streams, gRPC and TensorFlow Serving.
I had a new talk presented at “Codemotion Amsterdam 2018” this week. I discussed the relation of Apache…
At OOP 2018 conference in Munich, I presented an updated version of my talk about building scalable, mission-critical…
I am happy that my first official Confluent blog post was published and want to link to it from by blog: How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka
Apache Kafka + Kafka Streams to Produductionize Neural Networks (Deep Learning). Models built with TensorFlow, DeepLearning4J, H2O. Slides from JavaOne 2017.
I do a lot of presentations these days at meetups and conferences about how to leverage Apache Kafka and Kafka Streams to apply analytic models (built with H2O, TensorFlow, DeepLearning4J and other frameworks) to scalable, mission-critical environments. As many attendees have asked me, I created a video recording about this talk (focusing on live demos).