Phase 8: Production Hardening & Operations · 55 min · Python · Redis · Kubernetes
Performance Optimization & Scaling
Fast enough for production means p99 under the SLO.
Hiring signal: System design interviews test performance optimization for AI: candidates who describe semantic caching (60-80% cost reduction for enterprise KB), inference optimization (quantization, batching, speculative decoding), and p99 latency profiling pass. Candidates who don't know where the bottleneck is (embedding vs. retrieval vs. reranking vs. generation) fail. Horizontal scaling patterns for stateless AI servers are expected.
What you will learn
- Optimize inference: model quantization, batching, speculative decoding, prompt caching
- Implement semantic caching: caching responses to semantically similar queries for 60-80% cost reduction
- Configure connection pooling: database, API client reuse, vector DB connection management
- Scale horizontally: stateless API servers, queue-based workers, auto-scaling on request volume
- Profile performance: identifying bottlenecks (embedding vs. retrieval vs. reranking vs. generation), p99 optimization
What You'll Learn
This lesson takes approximately 55 min. By the end, you will be able to:
- Optimize inference: model quantization, batching, speculative decoding, prompt caching
- Implement semantic caching: caching responses to semantically similar queries for 60-80% cost reduction
- Configure connection pooling: database, API client reuse, vector DB connection management
- Scale horizontally: stateless API servers, queue-based workers, auto-scaling on request volume
- Profile performance: identifying bottlenecks (embedding vs. retrieval vs. reranking vs. generation), p99 optimization
The Problem
"Fast enough for production" means p99 latency under the SLO — not average, not p50, but the 99th percentile. Achieving this with LLM systems requires specific techniques: response streaming, semantic caching, batch processing, model routing (fast model for easy queries, frontier model for hard ones), and prompt length optimization.
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers Inference Optimization: Quantization, Batching, Speculative Decoding, Semantic Caching: 60-80% Cost Reduction, Model Routing: Fast Model for Easy, Frontier for Hard, Performance Profiling: Where's the Bottleneck?, Practical Application, What Hiring Managers Look For, Resources, Key Takeaways, Next Steps — plus a hands-on lab, quiz, and project artifact.
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