Apache Kafka in the Automotive Industry
Kai Waehner
In November 2019, I had the pleasure to visit “Motor City” Detroit. I met with several automotive companies,…
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Machine Learning
37 posts
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.
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.
Apache Kafka + KSQL + TensorFlow for Data Scientists via Python + Jupyter Notebook
Streaming Processing with Apache Kafka and KSQL for Data Scientists via Python and Jupyter Notebooks to build analytic models with TensorFlow and Keras.
Deep Learning Example: Apache Kafka + Python + Keras + TensorFlow + Deeplearning4j
“Python + Keras + TensorFlow + DeepLearning4j + Apache Kafka + Kafka Streams” => New example added to my “Machine Learning + Kafka Streams Examples” Github project.
MQTT + Apache Kafka => Video Recording from Kafka Summit Available
Processing IoT Data from End to End with MQTT and Apache Kafka => Video recording of my talk at Kafka Summit SF 2018 is online.
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.
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.
Deep Learning at Extreme Scale with the Apache Kafka Open Source Ecosystem
I had a new talk presented at “Codemotion Amsterdam 2018” this week. I discussed the relation of Apache…
Machine Learning Trends of 2018 combined with the Apache Kafka Ecosystem
At OOP 2018 conference in Munich, I presented an updated version of my talk about building scalable, mission-critical…
