IEEE：2025企业生成式AI峰会报告-Day2（英文版）-三个皮匠报告

1、August 20-21,2025San Jose,CAScaling AI driven Marketplace Products Dilip Patel,AI Product Lead Uber,ex-Amazon12 August 2025About Me&My Journey Data Scientist Turned Product Leader 10+years in scaling AI driven Search&Discovery,Monetization(Pricing,Promotions,Ads),Growth and Risk Products in Marketpl

2、aces/eCommerceBigTech(Uber,Amazon,Salesforce)+SmallTech/Startups(Groupon,Fractal.ai,WeWork)5+Years in Data Science and Quant Finance&Risk at Wall Street banksWhen no working.Student always:MBA,UC Berkeley;CS undergrad,Mumbai Univ;CFA Coach AI PM UC Berkeley and AI Product Advisor Startup Accelerator

3、sOutdoor:Ski,Water sports,Hiking,Biking,Tennis,Pickleball Live with the Family in the SF Bay Area A 5pillar framework to scale AI in Marketplaces Case studies Org design,governance&trust Takeaways Q&AWhat Well Cover Multi sided dynamics:buyers,merchants,earners,platform.Trade offs:GMV vs.margin/take

4、 rate,price competitiveness vs.P&L,speed vs.governance.Implication:You dont ship a modelyou ship a business outcome under constraints.The Marketplace Reality Start Practical prototype fast on APIs;scale with OSS/inhouse Data Moat exploit proprietary interaction data Human in the Loop augment experts

5、;capture overrides Optimize to KPIs objective functions features Trust&Control explainability,guardrails,fallback UXThe 5 Pillars(Framework Overview)Pillar 1:Start Practical,Scale SmartPhase 1(Weeks):Prove value with 3P APIs(OpenAI/Vertex)+thin glue.Phase 2(Quarters):Migrate to OSS(Llama/Mistral)or

6、bespoke for cost,latency,control.Case:Amazon newitem pricing started with ML benchmarking;success justified RL engine for price discovery&optimization.Moat model.Its clickstreams,comp signals,seller inventory,fulfillment patterns,UGC,returns.E.g.UGC and Contextual features embedding conversion lift

IEEE：2025企业生成式AI峰会报告-Day2（英文版）（99页）.pdf

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