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Using apache mx net in production deep learning streaming pipelines

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Using apache mx net in production deep learning streaming pipelines

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As a Data Engineer I am often tasked with taking Machine Learning and Deep Learning models into production, sometimes in the cloud and sometimes at the edge. I have developed Java code that allows us to run these models at the edge and as part of a sensor/webcam/images/data stream. I have developed custom interfaces in Apache NiFi to enable real-time classification against MXNet models directly through the Java API or through DJL.AI's Java interface. I will demo running models on NVIDIA Jetson Nanos and NVIDIA Xavier NX devices as well as in the cloud.



# Technologies Utilized:

# Apache MXNet, DJL.AI, NVIDIA Jetson Nano, NVIDIA Jetson XAVIER, Apache NiFi, MiNIFi, Java, Python.

As a Data Engineer I am often tasked with taking Machine Learning and Deep Learning models into production, sometimes in the cloud and sometimes at the edge. I have developed Java code that allows us to run these models at the edge and as part of a sensor/webcam/images/data stream. I have developed custom interfaces in Apache NiFi to enable real-time classification against MXNet models directly through the Java API or through DJL.AI's Java interface. I will demo running models on NVIDIA Jetson Nanos and NVIDIA Xavier NX devices as well as in the cloud.



# Technologies Utilized:

# Apache MXNet, DJL.AI, NVIDIA Jetson Nano, NVIDIA Jetson XAVIER, Apache NiFi, MiNIFi, Java, Python.

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