AI Cost Optimization: Caching, Batching & Right-sizing
Semantic caching, request batching, model cascading — techniques to slash AI infra bills (Day 24)
Senior Cloud, DevOps, MLOps & ML Platform Engineer | Heading Cloud, DevOps & MLOps for start-ups | AWS Container Hero | Educator | Mentor | Teaching Cloud, DevOps & Programming in Simple Way
Semantic caching, request batching, model cascading — techniques to slash AI infra bills (Day 24)
When to use managed vs self-hosted, a cloud-by-cloud breakdown for MLOps teams (Day 23)
A practical field guide to GPU node pools, model serving with vLLM and Triton, and the dark art of autoscaling inference workloads on GKE and EKS — without setting your cloud bill on fire. (Day 22)
Model versioning, prompt versioning, evaluation pipelines — the new ops discipline for AI (Day 21)
Auto-generating changelogs, test suggestions, deployment summaries, and anomaly detection (Day 20)
Real-world learning, production-grade projects, and AI insights for modern DevOps engineers
Example: Kubernetes, Terraform, Docker, AWS, MLOps...