Best Practice for Building AI Production Systems: From Inference to Deployment
_ScottCondron · x · 2026-08-23
This is a practical recommendation for developers aiming to improve their AI systems skills. The core idea is to take an open-weights model and build a complete end-to-end production system independently.
Key steps include:
- Understand the Inference Path: Grasp the computational work happening for every generated token.
- Build an Inference Server: Deploy the model using tools like vLLM or SGLang, expose it via an API, and handle multi-user concurrency.
- Performance Optimization: Deep dive into factors affecting inference speed, learning about batching, KV cache, quantization, GPU memory, and scheduling to balance speed, cost, and quality.
The scope can further extend to monitoring, evaluation, infrastructure, and cost optimization.
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