I built a scenario for a hybrid Machine Learning infrastructure leveraging Apache Kafka as a scalable central nervous system. The public cloud is used for training analytic models at extreme scale (e.g. using TensorFlow and TPUs on Google Cloud Platform (GCP) via Google ML Engine. The predictions (i.e. model inference) are executed on-premise at the edge in a local Kafka infrastructure (e.g. leveraging Kafka Streams or KSQL for streaming analytics). This post focuses on the on-premise deployment. I created a Github project with a KSQL UDF for sensor analytics. It leverages the new API features of KSQL to build UDF / UDAF functions easily with Java to do continuous stream processing on incoming events.


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java,machine learning,artificial intelligence,deep learning,kafka,mqtt,apache kafka,tensorflow,kafka streams,udf