Design Patterns for Running AIML and Bigdata Workloads on Kubernetes
summary
Claims that a full AI/ML + BigData stack (Spark, Flink, Hive, Kafka) runs reliably on Kubernetes with concrete patterns: use the operator pattern for lifecycle, Vault-secret integration instead of YAML secrets, and KNative scale-to-zero to cut cost spikes; run live image scans that auto-create vuln tickets and deploy fixes; use Alluxio cache in front of object storage and MTD tmpfs for spark/Flink scratch to avoid I/O bottlenecks; avoid monolithic “big data cluster” for predictable latency, enforce RBAC/network policies, and ensure log rotation so hosts don’t fill up.
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chapters (14)
- 0:00AIML on Kubernetes intro
- 5:26Benefits of AIML on K8s
- 7:13Traditional AIML cluster challenges
- 10:46Containers to Kubernetes
- 13:56Serverless with KNative
- 14:17AIML security patterns
- 17:27AIML CI/CD patterns
- 19:34AIML performance tuning
- 22:03AIML networking patterns
- 23:29K8s resource behavioral patterns
- 24:51Container composition patterns
- 27:25AIML pipeline storage
- 28:27Logging and monitoring
- 31:23Summary and recommendations
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