GenAI App Dev with TiDB and Amazon Bedrock скачать в хорошем качестве

GenAI App Dev with TiDB and Amazon Bedrock

GenAI app developers face numerous challenges, from managing multiple data sources and complex infrastructure to scaling unpredictable workloads. In this webinar, see how TiDB and Amazon Bedrock give you a clean, scalable backbone for GenAI app dev so you can ship features faster. We’ll show how to keep latency predictable as traffic surges, run transactional + vector/semantic queries on fresh data, and use Amazon Bedrock to power RAG, chat, and agents. You’ll also discover real-world patterns from leading AI companies, plus a live demo that ties it all together. What you’ll learn: Data layer for GenAI, minus the duct tape: How TiDB unifies OLTP + analytics + vector search to serve real-time context without ETL bloat. Hybrid retrieval that actually scales: Vector + keyword (hybrid) search on operational data for accurate, low-latency answers. Amazon Bedrock in practice: Use managed foundation models, orchestration, and guardrails to go from prototype to production safely. Battle-tested patterns: Find out what Dify and Manus learned about multi-tenant isolation, online schema changes, and keeping p95 steady during spikes. Speakers Erik Hanchett, Senior Developer Advocate, AWS Erik Hanchett is a Senior Developer Advocate at AWS. He’s the author of Vue.js in Action published by Manning and the Ember.js Cookbook. He’s been teaching software development for over 10 years on his YouTube channel, ‪@ProgramWithErik‬ that has over 100,000 subscribers. Eric’s given talks on frontend development from all around the world. Chris Hofmann, Growth Dev. of Data & AI, TiDB Chris Hofmann is a Growth Dev. of Data & AI at TiDB, specifically focused on helping developers accelerate the development of AI and emerging technology solutions. Chris brings years of experience from multiple AI unicorn startups, and has built his career on delivering impactful, unprecedented solutions to customers. He is responsible for leading over 100 generative AI proof of concepts across the public and private sectors, with project success featured in the U.S. Army University Press.