containerisation

An actionable checklist for AI virtualization: define goals and data needs; address compliance; size GPUs/CPUs, memory, and storage; choose hypervisors and container platforms; plan networking and GPU sharing; establish MLOps, CI/CD, and monitoring; secure models and access; control costs and right-size; benchmark and validate; design for portability, scalability, and lifecycle management from POC to production.

Outlines how to virtualize AI workloads effectively: pick the right mix of VMs and containers; plan CPU, GPU, memory and fast storage; use GPU passthrough/MIG, NUMA-aware placement and pinned scheduling; orchestrate with Kubernetes; keep data close for high IO; bake in security, isolation and governance; monitor performance and costs; and automate CI/CD for reproducible, scalable deployments.