Deep Learning in Real Time with TensorFlow, H2O.ai and Kafka Streams (Slides from JavaOne 2017)

Early October… Like every year in October, it is time for JavaOne and Oracle Open World in San Francisco… I am glad to be back at this huge event again. My talk at JavaOne 2017 was all about deployment of analytic models to scalable production systems leveraging Apache Kafka and Kafka Streams. Let’s first look at the abstract. After that I attach the slides and refer to further material around this topic.

Abstract “Deep Learning in Real Time with TensorFlow, H2O.ai and Kafka Streams”

Intelligent real time applications are a game changer in any industry. Deep Learning is one of the hottest buzzwords in this area. New technologies like GPUs combined with elastic cloud infrastructure enable the sophisticated usage of artificial neural networks to add business value in real world scenarios. Tech giants use it e.g. for image recognition and speech translation. This session discusses some real-world scenarios from different industries to explain when and how traditional companies can leverage deep learning in real time applications.

This session shows how to deploy Deep Learning models into real time applications to do predictions on new events. Apache Kafka will be used to inter analytic models in a highly scalable and performant way.

The first part introduces the use cases and concepts behind Deep Learning. It discusses how to build Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Autoencoders leveraging open source frameworks like TensorFlow, DeepLearning4J or H2O.

The second part shows how to deploy the built analytic models to real time applications leveraging Apache Kafka as streaming platform and Apache Kafka’s Streams API to embed the intelligent business logic into any external application or microservice.

Key Takeaways for the Audience: Kafka Streams + Deep Learning

Here are the takeaways of this talk:

• Focus of this talk is to discuss and show how to productionize analytic models built by data scientists – the key challenge in most companies.
• Deep Learning allows to build different neural networks to solve complex classification and regression scenarios and can add business value in any industry
• Deep Learning is used to build analytics models using open source frameworks like TensorFlow, DeepLearning4J or H2O.ai.
• Apache Kafka’s Streams API allows to embed the intelligent business logic into any application or microservice
• Apache Kafka’s Streams API leverages these Deep Learning Models (without Redeveloping) to act on new events in real time

Slides and Further Material around Apache Kafka and Machine Learning

Here are the slides of my talk:

https://www.slideshare.net/KaiWaehner/deep-learning-streaming-platform-with-kafka-streams-tensorflow-deeplearning4j-h2o

Some further material around Apache Kafka, Kafka Streams and Machine Learning:

• Blog Post: How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka
• Video: Build and Deploy Analytic Models with H2O.ai and Apache Kafka
• Code: Github Examples using Apache Kafka, TensorFlow, H2O, DeepLearning4J

I will post more examples and use cases around Apache Kafka and Machine Learning in the upcoming months… Stay tuned!