Production

Building a prototype in a notebook is easy. Serving it to thousands of users with low latency and reasonable cost is hard.

In this module, we dive deep into the engineering challenges of LLM serving. We cover the entire stack: from specialized serving engines like vLLM to advanced optimization techniques like quantization and flash attention, ending with critical safety guardrails.

Module Contents

1. LLM Serving

Understand the memory and compute bottlenecks of LLM inference. Learn why standard Python servers fail and how specialized engines like vLLM and TGI use Continuous Batching and PagedAttention to increase throughput by 10x.

2. Inference Optimization

Learn how to fit massive 70B models onto affordable hardware using Quantization (Int8, AWQ, GPTQ) and accelerate inference with Flash Attention and Speculative Decoding.

3. Safety and Moderation

Secure your LLM applications against jailbreaks, prompt injection, and PII leakage. Implement a robust “sandwich” architecture with Guardrails AI and Llama Guard.