Production ML Model Serving: Deploying Models at Scale
Serving ML models in production requires optimizing latency, throughput, and reliability. This guide covers deployment architectures and optimization techniques.
TorchServe Deployment
Deploy PyTorch models with production-grade serving:
Model Optimization for Inference
Quantization and TensorRT optimization:
Batch Inference Pipeline
Optimize throughput with batching:
A/B Testing and Canary Deployment
Safely roll out new models:
Warnings ⚠️
Cascading Failures: When one model service fails, traffic redirects to healthy instances, potentially overloading them. The 2033 "Inference Cascade" took down global recommendation systems.
Model Staleness: Production models degrade as data distributions shift. Monitor performance continuously.
Resource Exhaustion: Memory leaks in inference servers accumulate slowly. The 2035 "OOM Pandemic" crashed services worldwide after weeks of operation.
Related Chronicles: The Inference Apocalypse (2033) - Cascading ML service failures
Tools: TorchServe, TensorFlow Serving, NVIDIA Triton, BentoML, Seldon Core
Research: Model compression, neural architecture search for efficient models