Advanced~12 hours8 lessons

AI System Design for Engineers

Learn to architect production AI systems: model selection frameworks, semantic caching, cascade patterns, circuit breakers, load testing, and A/B testing AI features at scale.

What you'll build: a resilient, low-latency AI gateway

Start Course

Curriculum

Module A: Architecture Patterns

3 lessons

Lesson 1: Model Selection — Capability, Cost & Latency Trade-offs

Build a systematic decision framework for choosing the right model for each task. Understand the capability/cost/latency triangle and how to navigate it with benchmark data.

60 min

Lesson 2: Model Cascade & Fallback Architecture

Route simple requests to cheap fast models and complex requests to powerful models. Build a cascade that automatically escalates based on response confidence or task difficulty.

75 min

Lesson 3: Semantic Caching to Cut LLM Costs

Use vector similarity to cache LLM responses and serve cached answers for semantically equivalent queries — cutting API costs by 30–70% in production.

70 min

Recommended Watch

System Design for ML Engineers

Architecture patterns for ML systems — batch vs real-time inference, model serving, and scaling.

ByteByteGo11:34

Module B: Reliability & Scale

3 lessons

Lesson 4: Async & Batch Inference Patterns

Design non-blocking AI pipelines with async queues, background workers, and batch processing. Know when to serve synchronously vs. when to queue and poll.

65 min

Lesson 5: Circuit Breakers, Timeouts & Fallbacks

Build resilient AI services that degrade gracefully when the LLM API is slow or unavailable. Implement circuit breakers, configurable timeouts, and graceful fallback responses.

70 min

Lesson 6: Load Testing LLM Applications

Benchmark LLM API throughput, p50/p95/p99 latency, and error rates under realistic traffic. Use Locust to simulate concurrent users and identify bottlenecks before they hit production.

60 min

Recommended Watch

Caching for AI Systems

Semantic caching, circuit breakers, and rate limiting patterns for reliable AI infrastructure.

ByteByteGo8:47

Module C: Iteration & Deployment

2 lessons

Lesson 7: A/B Testing AI Features

Run rigorous A/B tests on prompts, models, and pipeline configurations. Understand statistical significance for AI quality metrics and avoid common pitfalls like metric gaming.

65 min

Lesson 8: Capstone — Design a Production AI System

End-to-end system design: select models for a multi-tier AI product, implement semantic caching and model cascade, add circuit breakers, load test the system, and document the architecture.

120 min

Recommended Watch

Blue-Green Deployments Explained

Zero-downtime deployment strategies for shipping ML model updates safely to production.

IBM Technology7:14

Course Quiz

5 questions · ~5 min · Pass 4/5 to unlock your badge

Start Quiz →