构建企业级 AI 平台的架构策略和实践-李欣.pdf

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1、Strategies of Machine Learning Platform Building&Practices in eBayeBay AIP Chief Architect,CCOE VAT Chairman/Bruce LiAgendaUnified data strategiesAI Platform vision,design principles and core capabilitiesAI/ML use case analysis123AI Use Cases-Online data services OTF FE-Streaming events NRT FE-Offli

2、ne batch/ETL datasets Batch FEStructured DataStructured DataSemi/Unstructured DataSemi/Unstructured Data(image/video/text/3D/(image/video/text/3D/)Data Source-Content generation/acquisition NRT pipelineStorageUnified online/offline feature storeUnified online/offline content storeData PiT ParityOnli

3、ne/offline PiT data strategiesPiT data parity is not requiredFeedback Loop-Short:Continuous online training-Long:Offline PiT feature simulationVendor/manual/auto labellingCommonDriver set&training set generation&management,catalog,data lineage,etc.CPU/GPU-CPU training and inferencing typically-GPU t

4、raining and inferencing typicallyChallenges of Building Enterprise ML PlatformTends to invest more on solutions instead of platformLack of clear boundary between solutions and platformLack of unified data strategies and self-service support for ML Platform buildingTraditionally focus more on trainin

5、g,lack of enough platform support on data/feature and inferencingLack of E2E seamless integration strategies cross feature,training and inferencingML Development LifecycleAgendaUnified data strategiesAI Platform vision,design principles and core capabilities123AI/ML use case analysis Our VisionTo em

6、power eBay AI practitioners to build,train and deploy machine learning models with fully-managed,efficient and self-service platform at scale.ML Platform Core Capability MapML Platform Architectural PrinciplesEnable self-service based on centralized configuration and metadata-driven design,with life