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IoT Live Demo – 100.000 Connected Cars with Kubernetes, Kafka, MQTT, TensorFlow

Live Demo – 100.000 Connected Cars – Real Time Processing and Analytics with Kubernetes, Kafka, MQTT and TensorFlow leveraging Confluent and HiveMQ.
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IoT and Supply Chain Optimization with Apache Kafka and Machine Learning
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Apache Kafka and Machine Learning for Real Time Supply Chain Optimization in IIoT

Apache Kafka and Machine Learning for Real Time Supply Chain Optimization: Integrate in real time with the legacy world and proprietary IIoT protocols (like Siemens S7, Modbus, Beckhoff ADS, OPC-UA, et al). You can process the data at scale and then ingest it into a modern database or analytic / machine  learning framework.
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Deep Learning KSQL UDF for Streaming Anomaly Detection of MQTT IoT Sensor Data

KSQL UDF for sensor analytics. Leverages the new API features of KSQL to build UDF / UDAF functions easily with Java to do continuous stream processing with Apache Kafka. Use Case: Connected Cars – Real Time Streaming Analytics using Deep Learning.
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Model Serving: Stream Processing vs. RPC / REST with Java, gRPC, Apache Kafka, TensorFlow

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.
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