New trends in data modernization: Catalysts for AI/ML analytics on AWS
summary
Claims a practical blueprint: industry-specific APIs for CPG pricing/availability, an ETHICA framework for AI ethics, and synthetic data that shortens one pharma R&D timeline from an expected 24–36 months to 24 months. Uses AWS Glue, DataBrew, DataZone, Bedrock, Neptune and marketplace plus Collibra/Alation integrations. Notes a telco lift costing $30–35M that stretches from 1.5 years to 3.5 years while producing a centralized data governance zone and Extreme LLM bots; fraud resolution improves ~15%, policy servicing 5–7%, client service ~3%.
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chapters (8)
- 0:00Introduction and keynote
- 2:55Modern data platform principles
- 10:07Data authenticity for AI models
- 17:19Centralized data governance
- 22:41AI augmentation with AWS
- 26:09Adaptability and augmented analytics
- 33:50Case study and wrap-up
- 35:59Q&A
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