Productivity & Planning

ML Deployment Pipeline

Cold storage to GPU inference — where deployment time goes, and where to claw it back.

90-210s
Total Time
65-165s
Critical Path
~30s
Parallelizable
~50s
Potential Savings

Pipeline execution timeline

0s
30s
60s
90s
120s
150s
180s
210s
Kubernetes Pod Provisioning
Critical Path 30-60s
Pod Init
↳ Container Image Pull
Parallel 10-30s
Image Pull
↳ Weight Cache Access
Parallel 5-15s
Cache
Weight Transfer & Mount
Critical Path 15-45s
Transfer
Container Initialization
Optimizable 10-20s
Init
Model Loading to VRAM
Critical Path 20-60s
VRAM Load

Critical path optimizations

Pod Provisioning (30-60s) Pre-provision warm GPU pods during off-peak hours
↓ Save 20-40s per deployment
Weight Transfer (15-45s) Use faster storage tier or co-locate weights with GPU nodes
↓ Save 10-25s per deployment
VRAM Loading (20-60s) Optimize model format (FP16, quantization) for faster loading
↓ Save 10-30s per deployment

Quick wins

Container Image Caching Pre-pull images to all GPU nodes, use smaller base images
↓ Save 10-30s (parallel task)
Python Import Optimization Lazy load ML libraries, optimize container startup
↓ Save 5-10s per deployment
Parallelization (Already Done) Image pull + cache access run during pod provisioning
✓ ~30s saved via parallelization