Data Science Popup Austin: Applied Machine Learning for IOT
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DATA SCIENCE POP UP AUSTIN Applied Machine Learning for IoT Hank Roark Data Scientist & Hacker, H20.ai hankroark
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Transcript of Data Science Popup Austin: Applied Machine Learning for IOT
DATA SCIENCEPOP UP
AUSTIN
Applied Machine Learning for IoT
Hank RoarkData Scientist & Hacker, H20.ai
hankroark
DATA SCIENCEPOP UP
AUSTIN
#datapopupaustin
April 13, 2016Galvanize, Austin Campus
Appl iedMachineLearningfor IoT
HankRoark
13-April-2016DataSciencePop-upAustin
WHOAMI I
Lead,[email protected]
JohnDeere:Research,SoftwareProductDevelopment,HighTechVenturesLotsoftimedealingwithdataoffofmachines,equipment,satellites,weather,radar,handsampled,andon.Geospatial,temporal/timeseriesdataalmostallfromsensors.
SystemsDesignandManagement:MITPhysics:GeorgiaTech
[email protected] @hankroarkhttps://www.linkedin.com/in/hankroark
WHYIOT?
Because it’s trending (so it must be promising)!
Internet of Things
Deep Learning
IF YOUAREINTODATA,THEIOTHASIT
LOTSOFDATA
LOTSOFDATA
GETREADYFORBRONTOBYTES!!
WOW,HOWBIGIS ABRONTOBYTE?
Image courtesy http://www.telecom-cloud.net/wp-content/uploads/2015/05/Screen-Shot-2015-05-27-at-3.51.47-PM.png
I OT ARCH I T EC TURE I S S E TUP FOR RE IN FORC ING , E XPONENT IA L GROWTH
MYPARTICULARCURIOSITY
EXAMPLEFROMTHEIOTDomain:PrognosticsandHealthManagementMachine:TurbofanJetEnginesDataSet:A.SaxenaandK.Goebel(2008)."TurbofanEngineDegradationSimulationDataSet",NASAAmesPrognosticsDataRepository
PredictRemainingUsefulLifefromPartialLifeRuns
Sixoperatingmodes,twofailuremodes,manufacturingvariability
Training:249jetenginesruntofailureTest:248jetengines
SENSORSSHOWTRENDSOVERTIME
Time
We also know remaining useful lifedecreases by at least one (1) cycle each operation.
How can one take advantage of this knowledge?
INCORPORATINGPRIORSTATE
Disentangling the dynamic core: a research program for a neurodynamics at the large-scale MICHEL LE VAN QUYEN Biol. Res. v.36 n.1 Santiago 2003 http://dx.doi.org/10.4067/S0716-97602003000100006
One option: Phase Space Embedding
Drawbacks: Incorporates knowledge from onlysmall number of prior states
Curse of dimensionality
Another parameter (tau)
KALMANFILTER
FINALPIPELINE
Featureengineering• Signalprocessing,featurecreation,featureselection
Regression Models• Supervised
MachineLearning(H2O)
PostProcessing• Kalmanfilter(pykalman,inthiscase)
Not really that much different than typical machine learning pipeline
BEFOREANDAFTERPOST-PROCESSING
CYCLE NUMBER CYCLE NUMBER
PR
ED
ICTE
D R
UL
UNIT #2UNIT #2
This presentation, data, and associated Jupyter notebooks (look in 2016_04_13_AppliedMachineLearningForIoT):https://github.com/h2oai/h2o-meetups/
WHYH2O
FLEETTELEMATICS:PREVENTIVEMAINTENANCE
PROBLEM
• H2Osupportforcustomer’sKerberosauthenticationmechanismforHadoop
• SupportforMapReduce,YARN,R,PythonandSparkinHadoop
• In-memory,distributedarchitecture • RapiddeploymenttoproductionwithPOJO • QuickprototypingwithH2OFlow
• Fleettelematics—analyzemaintenancerecordsandvehicleperformancetomakepredictionsonwhentodopreventivemaintenance
• Couldn’tscalebysamplingdata • Tookdaystocreatemodels
IMPACT
“AnnualSavingsare$7M” –Anonymized,MemberTechnicalStaff
• Whenyoulookatthecostoftowingastrandedvehicle,technicianlossofproductivity,andthecustomerlifetimevalue,theannualsavingsis$7M.”–Anonymized,LeadMemberTechnicalStaff
Leading Mobile Telecom Operator
Telecommunications
TAKE-AWAYS
• Knowyour“physics”o ”Physics”iswillbelikeafishfinderinthisseaofBrontobytes
o Lineardynamicsystems,Queues,Signals,etc.• BigdataandBigmodelsandSmallmodels• Keepingandeyeonpromisingnewmethods(e.g.,ConvolutionNeuralNetsandLSTM-RNN)
H2ORESOURCES
• Downloadandgo:http://www.h2o.ai/download• Documentation:http://docs.h2o.ai/• Booklets,Datasheet:http://www.h2o.ai/resources/• Github:http://github.com/h2oai/• Training:http://learn.h2o.ai/
THANKYOU
DATA SCIENCEPOP UP
AUSTIN
@datapopup #datapopupaustin
