Big Data and Machine Learning are key for innovation in many industries today. Large amounts of historical data are stored and analyzed in Hadoop, Spark or other clusters to find patterns and insights, e.g. for predictive maintenance, fraud detection or cross-selling.
This first part of the session explains how to build analytic models with R, Python and Scala leveraging open source machine learning / deep learning frameworks like Apache Spark, TensorFlow or H2O.ai. The second part discusses how to leverage these built analytic models in your own streaming applications or microservices; leveraging the Apache Kafka cluster and Kafka Streams instead of building an own stream processing cluster. The session focuses on live demos and teaches lessons learned for executing analytic models in a highly scalable and performant way.
The last part explains how Apache Kafka can help to move from a manual build and deployment of analytic models to continuous online model improvement in real time.