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Showing posts with the label deep learning

Harnessing the Data Lifecycle for Customer Experience Optimization: Streaming Classifications On Twitter Streams

Harnessing the Data Lifecycle for Customer Experience Optimization: Streaming Classifications For a deeper dive see this past webinar: available here. In the use case solved for this webinar, I am a Streaming Engineer at an airline, CloudAir.   I need to find, filter and clean Twitter streams then perform sentiment analysis. Score Models in the Stream to Act As the Streaming Engineer at CloudAIR I am responsible for ingesting data from thousands of sources, operationalizing machine learning models as part of our streams, running real-time ELT/ETL processes and building event processing systems running from devices, servers and edge nodes. For today’s use case, one of our ML engineers had given me a model that was deployed into one of our production Cloudera Machine Learning (CML) environments. I logged into Cloudera Data Platform (CDP), found the model, tested it, and then extracted the information I need to add this model to our streaming ingest flow f

Predicting Sensor Readings with Time Series Machine Learning

Predicting Sensor Readings with Time Series Machine Learning Sensors: Sensor Unit ( https://shop.pimoroni.com/products/enviro?variant=31155658457171 ) BME280 temperature, pressure, humidity sensor LTR-559 light and proximity sensor MICS6814 analog gas sensor ADS1015 ADC with spare channel for adding another analog sensor MEMS microphone 0.96-inch, 160 x 80 color LCD Unit Raspberry Pi 4 Intel Movidius 2 JDK 8 MiNIFi Java Agent 0.6.0 Python 3 See :    https://www.datainmotion.dev/2019/12/iot-series-minifi-agent-on-raspberry-pi.html See :     https://learn.pimoroni.com/tutorial/sandyj/getting-started-with-enviro-plus Source :    https://github.com/tspannhw/meetup-sensors/   https://github.com/tspannhw/ClouderaFlowManagementWorkshop    Example Data {"uuid": "rpi4_uuid_omi_20200417211935", "amplitude100": 0.3, "amplitude500": 0.1, "amplitude1000": 0.1, "lownoise": 0.1, "

Easy Deep Learning in Apache NiFi with DJL

Custom Processor for Deep Learning   Happy Mm.. FLaNK Day! I have been experimenting with the awesome new Apache 2.0 licensed Java Deep Learning Library, DJL.   In NiFi I was trying to figure out a quick use case and demo.   So I use my Web Camera processor to grab a still shot from my Powerbook webcam and send it to the processor.   The results are sent to slack. Since it's the holidays I think of my favorite holiday movies:   The Matrix and Blade Runner.   So I thought a Voight-Kampf test would be fun.   Since I don't have a Deep Learning QA piece built yet, let's start by seeing if you look human.  We'll call them 'person'.   I am testing to see if I am a replicant.  Sometimes hard to tell.   Let's see if DJL thinks I am human. See :    http://nautil.us/blog/-the-science-behind-blade-runners-voight_kampff-test Okay, so it least it thinks I am a person.   The classification of a Christmas tree is vaguely accurate. It did