Download pdf - Using the SplunkMachine Learning Toolkit to Create … Create Your Own Custom Models ... • Multi-algorithm support across ... – Extending SPL with Custom Search Commands and the

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Page 1: Using the SplunkMachine Learning Toolkit to Create … Create Your Own Custom Models ... • Multi-algorithm support across ... – Extending SPL with Custom Search Commands and the

Copyright©2016Splunk Inc.

Dr.AdamOlinerDirectorofEngineering,DataScience,Splunk

UsingtheSplunk MachineLearningToolkittoCreateYourOwnCustomModels

ManishSainaniPrincipalProductManager,Splunk

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Disclaimer

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Duringthecourseofthispresentation,wemaymakeforwardlookingstatementsregardingfutureeventsortheexpectedperformanceofthecompany.Wecautionyouthatsuchstatementsreflectourcurrentexpectationsandestimatesbasedonfactorscurrentlyknowntousandthatactualeventsorresultscoulddiffermaterially.Forimportantfactorsthatmaycauseactualresultstodifferfromthose

containedinourforward-lookingstatements,pleasereviewourfilingswiththeSEC.Theforward-lookingstatementsmadeinthethispresentationarebeingmadeasofthetimeanddateofitslivepresentation.Ifreviewedafteritslivepresentation,thispresentationmaynotcontaincurrentoraccurateinformation.Wedonotassumeanyobligationtoupdateanyforwardlookingstatementswemaymake.Inaddition,anyinformationaboutourroadmapoutlinesourgeneralproductdirectionandissubjecttochangeatanytimewithoutnotice.Itisforinformationalpurposesonlyandshallnot,beincorporatedintoanycontractorothercommitment.Splunkundertakesnoobligationeithertodevelopthefeaturesor

functionalitydescribedortoincludeanysuchfeatureorfunctionalityinafuturerelease.

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Whoarewe?

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Dr.AdamOliner– DirectorofEngineering,DataScience&MachineLearning– Splunker for2years– Embarrassinglyovereducated

ManishSainani– PrincipalProductManager,MachineLearning– Splunker for2years– FirstMLhireatSplunk!

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Whatarewedoinghere?

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OverviewofMachineLearningTheAssistants:GuidedMachineLearning– Prepare– Fit– Validate– Deploy

Examples– DIYAnomalyDetector– CustomerApplications

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OverviewofMLatSplunk

CorePlatformSearch PackagedPremiumSolutions CustomML

PlatformforOperationalIntelligence

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SplunkMachineLearningToolkit

Assistants: Guidemodelbuilding,testing,&deployingforcommonobjectivesShowcases: InteractiveexamplesfortypicalIT,security,business,IoTusecases

Algorithms: 25+standardalgorithmsavailableprepackagedwiththetoolkitSPLMLCommands:Newcommandstofit,testandoperationalizemodelsPythonforScientificComputingLibrary:300+opensourcealgorithmsavailableforuse

Buildcustomanalyticsforanyusecase

ExtendsSplunkplatformfunctionsandprovidesaguidedmodelingenvironment

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What’sNewsinceour0.9BetaRelease(lastyear’s.conf)?

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• Newnameandabbreviation;-)• Noeventlimits(removalof50Klimitonfittingmodels)

• Configurableresourcecapsviamlspl.conf

• Searchheadclusteringsupport• Distributed/streamingapply• Scheduledfit• Newalgorithms(nextslide)

– Featureengineeringandselection– Stochasticgradientdescent(e.g.)– ARIMA

• Multi-algorithmsupportacrossAssistants

• Scatterplotmatrixviz• Alerting• Tooltips• In-apptours• ClusterNumericEventsassistant• VideosvideosvideosforeachassistantacrossIT,Security,IoT andBusinessAnalytics

• ML-SPLCheatSheet

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Algorithmssupported(v2.0,.conf2016)

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TheAssistants:GuidedMachineLearning

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MachineLearning

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AprocessforgeneralizingfromexamplesExamples– A,B,…→ # (regression)– A,B,... → a (classification)– Xpast → Xfuture (forecasting)– likewithlike (clustering)– |Xpredicted – Xactual|>>0 (anomalydetection)

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MachineLearningProcess

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CollectData

Explore/Visualize

Model

Evaluate

Clean/Transform

Publish/Deploy

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MachineLearningProcesswithSplunk

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CollectData

Explore/Visualize

Model

Evaluate

Clean/Transform

Publish/Deploy

props.conf,transforms.conf,DatamodelsAdd-onsfromSplunkbase,etc.

Pivot,TableUI,SPLMLToolkit

Alerts,Dashboards,Reports

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DomainExpertise(IT,Security,…)

DataScienceExpertise

SplunkExpertise

CustomMachineLearning– SuccessFormula

Identifyusecases

Drivedecisions

Setbusiness/opspriorities

SPL

Dataprep

Statistics/mathbackground

Algorithmselection

Modelbuilding

SplunkMLToolkitfacilitatesandsimplifiesviaexamples&guidance

Operationalsuccess

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GuidedMLwiththeAssistants

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Guidesyouthroughvariousanalytics– Prepare,fit,validate,anddeploy

AutomaticallygeneratesalltherelevantSPL

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Assistants:Fit

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Assistants:Validate

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Assistants:Deploy

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TheAssistants

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1. PredictNumericFields2. PredictCategoricalFields3. DetectNumericOutliers4. DetectCategoricalOutliers5. ForecastTimeSeries6. ClusterNumericEvents

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PredictNumericFields

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Algorithms– LinearRegression

ê …includingLasso,Ridge,andElasticNet– KernelRidge– DecisionTreeRegressor– RandomForestRegressor– SGDRegressor

Validation– Fourvisualizationsofpredictionerror– R2 andRMSE

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PredictCategoricalFields

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Algorithms– LogisticRegression– DecisionTreeClassifier– RandomForestClassifier– SGDClassifier– SVM– NaïveBayes

ê BernoulliNB andGuassianNB

Validation– Precision,recall,accuracy,F1– Confusionmatrix

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DetectNumericOutliers

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Methods– Standarddeviation– Medianabsolutedeviation– Interquartilerange

Validation:

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DetectCategoricalOutliers

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StatisticalmethodsValidation:

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ForecastTimeSeries

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Algorithms– State-spacemethodusingKalman filter– ARIMA

Validation

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ClusterNumericEvents

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Algorithms– KMeans– DBSCAN– Birch– SpectralClustering

Validation– ScatterplotMatrixviz

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Prepare

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DataGatheringandPrep

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Source:CrowdFlower

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Splunk!

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Leadingplatformforcollecting,cleaning,andtransformingdataInteractiveFieldExtractorDatamodelsHundredsofadd-onsfromSplunkbasetransforms.confprops.confetc.

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FeatureEngineeringTFIDF(term-frequencyxinversedocument-frequency)– Transformfree-formtextintonumericattributes

StandardScaler (i.e.normalization)FieldSelector (i.e.choosekbestfeaturesforregression/classification)PCAandKernelPCA

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PreprocessingintheAssistants

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Fit

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Fit:What’sNew

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NoeventlimitsConfigurableresourcecaps(ml-spl.conf)SearchheadclusteringsupportScheduledfitNewalgorithms

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Fit:What’sNew

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Validate

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Validate/Apply:What’sNew

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ConfigurableresourcecapsSearchheadclusteringsupportDistributed/streamingapplyScatterplotmatrixviz

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ScatterplotMatrixViz

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Deploy

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DeployanywhereinSplunk!

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ScheduledtrainingAlertingReportsanddashboardsAugmentedsearchresultsetc.

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Deploy:What’sNew

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DistributedApply– Applymodelstoindexeddata– Streaming

ScheduledtrainingAlerting

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What’sNew:ScheduledFit

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What’sNew:Alerting

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Example:DIYAnomalyDetector

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Let’sBuildanAnomalyDetector!

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We’llusetwoAssistants– PredictNumericFields– DetectNumericOutliers

Showautomatically-generatedintermediateSPL

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FitaPredictiveModel

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SetupScheduledTraining

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OpenResidualsinSearch

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OpenDetectNumericOutliersAssistant

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DetectOutliers(LargePredictionErrors)

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ScheduleanAlert

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ScheduleanAlert

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ScheduleanAlert

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ManageYourNewAnomalyDetector

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TheAssistantGeneratedtheSPLforYou

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TheAssistantGeneratedtheSPLforYou

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YouBuiltanAnomalyDetector!

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YoubuiltapredictivemodelofACPowerWhenthepredictionerrorfromthismodelisanoutliercomparedtopasterrors,yougenerateanalertThispredictivemodelautomaticallyretrainsitselfonascheduleyoucontrolYoudidn’thavetotypeanySPL

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#winning

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MachineLearningCustomerSuccess

NetworkOptimizationDetect&PreventEquipmentFailure Security/FraudPrevention

PrioritizeWebsiteIssuesandPredictRootCause

PredictGamingOutagesFraudPrevention

MachineLearningConsultingServices AnalyticsAppbuiltonMLToolkit

Optimizingoperationsandbusinessresults

PreventCellTowerFailureOptimizeRepairOperations

Entertainment Company

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MachineLearningToolkitCustomerUseCases

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Speedingwebsiteproblemresolutionbyautomaticallyrankingactionsforsupportengineers

Reducingcustomerservicedisruptionwithearlyidentificationofdifficult-to-detectnetworkincidents

Minimizingcelltowerdegradationanddowntimewithimprovedissuedetectionsensitivity

Improvinguptimeandloweringcostsbypredicting/preventingcelltowerfailuresandoptimizing repairtruckrolls

Predictingandavertingpotentialgamingoutageconditionswithfiner-graineddetection

EnsuringmobiledevicesecuritybydetectinganomaliesinIDauthentication

PreventingfraudbyIdentifyingmaliciousaccountsandsuspiciousactivitiesEntertainment Company

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DetectNetworkOutliersReduceddowntime+increasedserviceavailability=bettercustomersatisfaction

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MLUseCase Monitornoiserisefor20,000+celltowerstoincreaseserviceanddeviceavailability,reduceMTTR

Technicaloverview • Acustomizedsolutiondeployedinproductionbasedonoutlierdetection.• Leveragepreviousmonthdataandvotingalgorithms

“TheabilitytomodelcomplexsystemsandalertondeviationsiswhereITandsecurityoperationsareheaded…SplunkMachineLearninghasgivenusaheadstart...”

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ReliablewebsiteupdatesProactivewebsitemonitoringleadstoreduceddowntime

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“SplunkMLhelpsusrapidlyimproveend-userexperiencebyrankingissue severitywhichhelpsusdeterminerootcausesfasterthusreducingMTTRandimprovingSLA”

• Veryfrequentcodeandconfig updates(1000+daily)cancausesiteissues• Finderrorsinserverpools,thenprioritizeactionsandpredictrootcause

• CustomoutlierdetectionbuiltusingMLToolkitOutlierassistant• BuiltbySplunkArchitectwithnoDataSciencebackground

MLUseCase

Technicaloverview

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WhatNow?

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GettheMachineLearningToolkitfromSplunkbaseGowatchMachineLearningVideosonSplunkYoutube Channelhttp://tiny.cc/splunkmlvideosGotoMachineLearningstalks:– AdvancedMachineLearninginSPLwiththeMachineLearningToolkitbyJacobLeverich– ExtendingSPLwithCustomSearchCommandsandtheSplunkSDKforPythonbyJacobLeverich

SeveralCustomersandPartnerTalks– Cisco,Scianta Analytics,AsianTelco,etc.EarlyAdopterAndCustomerAdvisoryProgram:[email protected]:[email protected]:[email protected]

http://tiny.cc/splunkmlapp

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THANKYOU


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