four moves to machine learning at scale

FourMovestoScalableMachineLearningRobertWelborn,USAA

Movefrommonolithstodistributedcompute• Buildsthebasisforaccesstotoolsacrosstheenterpriseinsteadofonappliances

• Pointyouranalyticinfrastructureatoperationaldatainsteadofanalyticdata

• Transactionaldatadrivesyourmarketingmodels

MovefromBatchtoRealTime• Thelatencyisn’ttheproblem,thebatchphilosophyistheproblem• Processwhentheinformationisavailable,notonaschedule,thespeedwillgetbetterasyourprocessesimprove• Modelsthathaveworkedforyearswillneedtoberetooled(scoringcan’ttakeallnight)

MovefromStaticCoefficientstoResponsive

• Createtheabilityforfastfeedbackacrosstheenterprise

• The“learningloop”themodellearnsfromtheresultsofthelastexperiment

• Ifyouareinaregulatedindustry,youhavetobringyourcompliancefolksalongforthisjourney

• Humansmakedecisionsaboutwhattotestnotwhattoshowwhom

MovefromonthePremisestotheCloud• Don’treinventscaling,scalingwasdesignedforthecloud,atbest,youaretryingtodothingsbetterthanpeoplewhoalreadysolvedtheproblem,atworst,you’re“scaling”unsustainably• Allocatetherighttasksinthecloudandonpremises