Resilience in the Uncharted AI Landscape
summary
Claim: resilience requires hundreds of early, granular decisions and can be engineered into a genAI chat/search pipeline built with containerized agents on GKE, Terraform, Kubernetes, Docker, CI/CD (GitHub Actions/Jenkins), observability (ELK/Prometheus/Grafana) and SCA/SAST/CSPM tooling. Example: a grocery chain’s chat feeds RAG LLMs, agentic workflows and inventory models; incidents use rate limits, key rotation, triage, rollback to last known good, validate each agent, then reintroduce traffic gradually. Compliance and backups guide scope; resilience by design avoids catastrophic downtime. containerized agents enable isolation.
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chapters (22)
- 0:00Introduction to resilience
- 1:04Defining resilience pillars
- 4:16Five tenets of cyber security
- 5:19Five resilience fundamentals
- 6:26Key players in resilience
- 7:08Building a resilient AI product
- 10:18Product architecture decisions
- 11:42Infrastructure design choices
- 13:04AI product and deployment
- 14:48Observability and security tools
- 16:12Security privacy compliance
- 16:55Backup and storage planning
- 17:38Cost optimization strategies
- 18:20Scenario planning in red
- 19:27Balancing savings and growth
- 20:32Predictive risk management
- 21:57Incident response process
- 24:02Recovery process checklist
- 26:48Plasticity and lessons learned
- 27:29Proving resilience to customers
- 28:54Key takeaways summary
- 30:02Q&A session
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