real-time deeplearning on iot sensor data
TRANSCRIPT
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 it’s 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 µA• Cost 5 US$
• 600 mA/h• 70 days• 1 measurement /h• Cost 2 US$
• Energy consumption• Standby 3µA• 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
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/
• 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