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文档简介
Strategies
ofMachineLearningPlatformBuilding&Practicesin
eBayAgendaAIPlatformvision,designprinciplesandcore
capabilities1AI/MLuse
caseanalysis23Unified
datastrategiesAIUse
CasesOnlinedataservices–OTF
FEStreamingevents–NRTFEOfflinebatch/ETLdatasets–Batch
FEStructured
DataSemi/Unstructured
Data(image/video/text/3D/…)Data
Source- Contentgeneration/acquisitionNRT
pipelineUnifiedonline/offlinefeature
storeUnifiedonline/offlinecontent
storeStorageDataPiT
ParityOnline/offlinePiTdata
strategiesPiTdataparityisnot
requiredFeedback
LoopShort:ContinuousonlinetrainingLong:OfflinePiTfeature
simulationVendor/manual/auto
labellingCommonDriver
set &trainingsetgeneration&management,catalog,datalineage,
etc.CPU/GPU- CPUtrainingandinferencing
typically- GPUtrainingandinferencing
typicallyChallengesofBuildingEnterpriseML
PlatformTendstoinvestmoreonsolutionsinsteadof
platformLackofclearboundarybetween
solutionsand
platformLackofunifieddatastrategiesandself-servicesupportforMLPlatform
buildingTraditionally
focusmoreontraining,lackofenoughplatformsupportondata/featureand
inferencingLackofE2Eseamlessintegrationstrategies
crossfeature,trainingand
inferencingMLDevelopment
LifecycleAgendaAIPlatform
vision,designprinciplesandcorecapabilities23Unified
datastrategies1AI/MLuse
caseanalysisOur
VisionToempowereBayAIpractitionerstobuild,trainanddeploymachinelearningmodelswithfully-managed,efficientandself-serviceplatformat
scale.MLPlatformCoreCapability
MapMLPlatformArchitectural
PrinciplesEnableself-servicebasedoncentralizedconfigurationandmetadata-drivendesign,
withlifecyclemanagementandgovernancein
placeEnableunifiedmetadataanddefinitionscrossonlineandoffline,withenoughflexibility andextensibilitytosupportdomainlevel
customizationsProvideagroupofmanagementAPIs&servicesforMLPmanagedlifecycle,andenabletheE2Eseamlessintegrationbasedonthe
APIsProvideunifiedcatalogs(includingdata,storedvariables,features,models,solutions,etc.)topromotediscovery,reuseandbetter
governanceProvideE2EdatalineagesfortheAIPlatformdomain
entitiesApplyunifiedmonitoringcrossthewholeML
platformMLPlatformOnlineIntegration
ArchitectureEntityModelinginML
PlatformDependencyDAG&Execution
PlanUnifiedCPU/GPUInferencing
PlatformModelandFeature
MonitoringAgenda3Unified
datastrategies21AI/MLuse
caseanalysisAIPlatformvision,designprinciplesandcore
capabilitiesWhyDataStrategiesaresoImportantfor
AI/MLImagesource:Cognilytica,from
https://www.ayadata.ai/blog-posts/manual-vs-automated-data-labelingBatch
FeatureFeature
DSLNRTRoll-up
AbstractionNRTFeature
EngineeringNRT
FeatureSchemaEvent
processingDerived
ComputationOn-the-fly
FeatureComparisonsofDifferentFeatures
TypesBatch
Feature NRT
FeatureOn-the-fly
FeatureOnline/offlinePiT
StrategyPiTSimulation/FeatureSnapshottingPiTSimulation/FeatureSnapshottingFeatureSnapshotting
OnlyReusabilityEasyto
reuseEasyto
reuseSolutionbysolution
supportTime-to-MarketFastFastexceptnewenrichedevent
acquisitionSlowMLP
ManagedSelf-servicebyEndUsers(DS)DelayofData
FreshnessData
SourceYesYesYesYesNoNo1Day+P99<5
secReal-timeETL/Batchdata/Snapshotted
DatasetEnriched
eventsRequestcontext
/Onlinedata
servicesEmbracingNRT
StrategyIntegratedData
StrategiesFeature
PlatformUnifiedFeature
StoreFeatureLifecyle
Mngt.FeaturePiT
SimulationTraining
PlatformTrainingSetGeneratio
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