02基于元数据和配置驱动的 eBay 交易风控 AI 模型管理和部署实践--王兵.pdf

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1、Model Spec Driven AI Model Management&Deployment at eBay Payments RiskBing WangeBay Payments&RiskAgendaeBay Payments Risk AI Model Lifecycle and Model SpecUnified Context for Model Training&Model ServingModel Integration&Deployment by Model SpecModel Serving Observability&Monitoring1234eBay Payments

2、 Risk AI Model Lifecycle and Model SpecFeature EngineeringModel TrainingModel DeploymentPerformance ValidationBusiness UsageModel Refresh(Refit)OfflineOnlinePayments Risk AI Model Lifecycle Feature EngineeringModel TrainingModel DeploymentPerformance ValidationBusiness UsageModel Refresh(Refit)Offli

3、neOnlineMetadata in AI Model Lifecycle Raw Features MetadataTraining Dataset Metadata,Pipeline Metadata,Model Object Metadata,Model Service API Metadata,Features Fetching Metadata,Feature Preprocessing Metadata,Model Prediction Metadata,Model Output Post-processing MetadataFeature EngineeringModel T

4、rainingModel DeploymentPerformance ValidationBusiness UsageModel Refresh(Refit)OfflineOnlineRaw Features MetadataTraining Dataset Metadata,Pipeline Metadata,Model Object Metadata,Model Service API Metadata,Features Fetching Metadata,Feature Preprocessing Metadata,Model Prediction Metadata,Model Outp

5、ut Post-processing MetadataMetadata in AI Model Lifecycle Model Spec(Model Specification)Metadata Group-Model SpecModel Spec(Model Specification)Basic Model Information:owners,model type,scenario,refresh frequency,.Feature Fetching:feature name,data source,value type,default value,.Model Object:mode

6、l type,framework and version,parameters,target SLA,Inference Preprocessing&Post-processing:dependent raw features,feature preprocessing expression,model output mapping logics,Multi-models Inference:pipeline definition,model routing definitionMonitoring and Logging:schema definition,metrics,event/tab