real-time deeplearning on iot sensor data

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  • How to capture, analyse and react on IoT generated sensor data in real time

    Romeo Kienzler, Chief Data Scientist, IBM Watson IoT, WW

  • Why IoT (now) ? 15 Billion connected devices in 2015

    40 Billion connected devices in 2020

    World population 7.4 Billion in 2016

  • Why IoT (now) ? 2016 90% of all data generated WW is at the

    edge of an IoT device

    This data is never

    captured

    analysed

    acted on

  • Why IoT (now) ? 60% of data looses its value within milliseconds

    of being generated

    New generation of Sensors

    low cost

    low energy consumption

    low data transmission cost

    long life batteries / self supplementary

  • Energy consumption 0.33333333 A Cost 5 US$

    600 mA/h 70 days 1 measurement /h Cost 2 US$

    Energy consumption Standby 3A Rx 30 mA Tx 53 mA

    Range 800m Cost 50 US$

  • Why IoT (now) ? If a tree falls in the forest we will hear it

    IBM announced to invest 3 billion US$

    Opened IBM Watson IoT Global HQ in Munich, Germany

    As of 2015

    4000 IoT clients 170 countries 1400 partners 750 IoT patents 1000 Emloyees in HQ

  • IBM and Siemens IBM partners with Siemens Buildings

    Technologies Division to maximise the potential of connected buildings

    by the data they create (private side note)

  • IBM and KONE IBM partners with KONE on Cloud-based

    Embedded intelligence in elevators and escalators

  • IBM and KONE IBM partners with KONE on Cloud-

    based Embedded intelligence in elevators and escalators

  • How 2 IoT?

  • How 2 IoT?

    What is MQTT? light weight telemetry protocol Publish-Subscribe protocol via Message Broker Invented by IBM 1999 OASIS Standard since 2013

  • How 2 IoT?

  • How 2 IoT?

  • ApacheSparkthe state-of-the-art in cloud based analytics

    Storage Layer (OpenStack SWIFT / Hadoop HDFS / IBM GPFS)

    Execution Layer (Spark Executor, YARN, Platform Symphony)

    Hardware Layer (Bare Metal High Performance Cluster)

    GraphXStreaming SQL MLLib BlinkDB R MLBaseY O U

    Intel Xeon E7-4850 v2 48 core, 3 TB RAM, 72 GB HDD, 10Gbps

    S T R E A M S

  • Machine Learning on historic data

    Source: deeplearning4j.org

    http://deeplearning4j.org

  • Online Learning

    Source: deeplearning4j.org

    http://deeplearning4j.org

  • online vs. historic Pros

    low storage costs

    real-time model update

    Cons

    algorithm support

    software support

    no algorithmic improvement

    compute power to be inline with data rate

    Pros

    all algorithms

    abundance of software

    model re-scoring / re-parameterisation (algorithmic improvement)

    batch processing

    Cons

    high storage costs

    batch model update

  • DeepLearningDeepLearning

    Apache Spark

    Hadoop

  • Neural Networks

  • Neural Networks

  • Deeper (more) Layers

  • Convolutional

  • Convolutional

    + =

  • Convolutional

  • Learning of a function

    A neural network can basically learn any mathematical function

  • Recurrent

  • LSTM

  • http://karpathy.github.io/2015/05/21/rnn-effectiveness/

    http://karpathy.github.io/2015/05/21/rnn-effectiveness/

  • Outperformed traditional methods, such as cumulative sum (CUSUM) exponentially weighted moving average (EWMA) Hidden Markov Models (HMM)

    Learned what Normal is Raised error if time series pattern haven't been seen before

  • Learning of a program

    A LSTM network is touring complete

  • Problems Neural Networks are computationally very complex especially during training but also during scoring

    CPU (2009) GPU (2016) IBM SyNAPSE (2018)

  • DeepLearningthe future in cloud based analytics

    Storage Layer (OpenStack SWIFT / Hadoop HDFS / IBM GPFS)

    Execution Layer (Spark Executor, YARN, Platform Symphony)

    Hardware Layer (Bare Metal High Performance Cluster)

    GraphXStreaming SQL MLLib BlinkDBDeepLearning4J ND4J

    R MLBase H2OY O U

    GPUAVX

    Intel Xeon E7-4850 v2 48 core, 3 TB RAM, 72 GB HDD, 10Gbps, NVIDIA TESLA M60 GPU

    (cu)BLAS

    jcuBLAS

    S T R E A M S

  • Why IoT (now) ?Formal Definition (Romeo Kienzler, 2016)

    Cognitive IoT maximises efficiency of the system under observation by measuring all relevant parameters in order to (re)act accordingly to

    push the system into a state near to the global optimum

  • My Vision

    What if the majority of cars where connected and sensed? What if we can detect a state of

    unpreventable accidents? What if in such a case we just issue a 30% brake command to all

    vehicles? Still a dream?

  • Do it yourself DeepLearning Architecture on-click cloud

    deployment

    to be published:http://www.ibm.com/developerworks/analytics/

    to be announced:Twitter: @romeokienzler

    Find this talk on youtube:http://ibm.biz/romeokienzler

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