four moves to machine learning at scale

5
Four Moves to Scalable Machine Learning Robert Welborn, USAA

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Page 1: Four Moves to Machine Learning at Scale

FourMovestoScalableMachineLearningRobertWelborn,USAA

Page 2: Four Moves to Machine Learning at Scale

Movefrommonolithstodistributedcompute• Buildsthebasisforaccesstotoolsacrosstheenterpriseinsteadofonappliances

• Pointyouranalyticinfrastructureatoperationaldatainsteadofanalyticdata

• Transactionaldatadrivesyourmarketingmodels

Page 3: Four Moves to Machine Learning at Scale

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

Page 4: Four Moves to Machine Learning at Scale

MovefromStaticCoefficientstoResponsive

• Createtheabilityforfastfeedbackacrosstheenterprise

• The“learningloop”themodellearnsfromtheresultsofthelastexperiment

• Ifyouareinaregulatedindustry,youhavetobringyourcompliancefolksalongforthisjourney

• Humansmakedecisionsaboutwhattotestnotwhattoshowwhom

Page 5: Four Moves to Machine Learning at Scale

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