How can you leverage the flexibility and extreme scale in the public cloud combined with your Apache Kafka ecosystem to build scalable, mission-critical machine learning infrastructures, which span multiple public clouds or bridge your on-premise data centre to cloud? This talk will discuss and demonstrate how you can leverage technologies such as TensorFlow with your Kafka deployments to build such a machine learning infrastructure for ingesting, preprocessing, training, deploying, and monitoring analytic models.

The discussed architecture will include capabilities like scalable data preprocessing for training and predictions, combination of different Deep Learning frameworks, data replication between data centres, intelligent real time microservices running on Kubernetes, and local deployment of analytic models for offline predictions.