A hands-on deep dive on using Apache Pulsar Apache NiFi with Apache MXNet, OpenVino, TensorFlow Lite, and other Deep Learning Libraries on the actual edge devices including Raspberry Pi with Movidius 2, Google Coral TPU and NVidia Jetson Nano. We run deep learning models on the edge devices and send images, capture real-time GPS and sensor data. With our low coding IoT applications providing easy edge routing, transformation, data acquisition and alerting before we decide what data to stream real-time to our data space. These edge applications classify images and sensor readings real-time at the edge and then send Deep Learning results to Apache NiFi for transformation, parsing, enrichment, querying, filtering and merging data to various data stores. https://www.datainmotion.dev/2019/08/updating-machine-learning-models-at.html
OPEN TALK (AI): Utilizing Apache Pulsar, Apache NiFi and MiNiFi for EdgeAI IoT at Scale
Tim Spann is a Developer Advocate @ StreamNative where he works with Apache Pulsar, Apache Flink, Apache NiFi, Apache MXNet, TensorFlow, Apache Spark, big data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, streaming technologies, and Java programming. Previously, he was a Principal Field Engineer at Cloudera, a Senior Solutions Architect at AirisData and a senior field engineer at Pivotal. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on big data, the IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as IoT Fusion, Strata, ApacheCon, Data Works Summit Berlin, DataWorks Summit Sydney, and Oracle Code NYC. He holds a BS and MS in computer science.