Building Resilient AI Platforms at Scale
summary
Teams use Terraform or cloud formation, GitOps, Nvidia Docker runtime and Kubernetes device plugins, Promethius plus graphina and OpenTelemetry for full-stack observability, and chaos mesh for injected failures and validation. Concrete results claim 3–5x throughput, latency improvements from 200 ms → 15 ms, and operational gains like 99.9% uptime with deployments in hours and 20–40% cost reduction through GPU batching, caching, Triton-style serving, blue/green and canary rollout patterns.
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chapters (11)
- 0:00Introduction to AI Platforms
- 0:17Engineering Practices for AI Success
- 0:40Challenges in AI Platform Development
- 1:19Foundational Principles for AI Infrastructure
- 1:58Containerization and GPU Enablement
- 2:35Kubernetes Orchestration in Practice
- 3:06Deployment Patterns for Reliability
- 3:39Optimizations for Performance
- 4:17Comprehensive Observability
- 5:33Ensuring Resilience and Fault Tolerance
- 6:40Business Impact and Conclusion
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