Learning
Build durable engineering knowledge
Follow structured courses, work through each chapter, and keep your progress in one place.
AI Infra: Serving LLMs at Scale
This course teaches how LLM inference uses GPUs, from memory, batching, parallelism, and quantization to routing, capacity, and cost, and how to design a serving system that meets latency targets.
AI Agents: Design Patterns and Architecture
This course teaches how to design LLM agents that hold up in production: choosing loop patterns, tools, context and memory, multi-agent splits, and the reliability and security controls around them.
SRE & Reliability Engineering 101
This course teaches how to define reliability with SLOs, alert and debug from telemetry, respond to and learn from incidents, and change and scale production systems without breaking them.
LLM Engineering 101
This course teaches how to build reliable products on top of large language models: prompting, tool calling, retrieval, agents, evaluation, safety, and keeping cost and latency under control.
A/B Testing 101
This course teaches how to design, size, and analyze online experiments, catch the traps that invalidate them, and turn the results into a defensible ship or no-ship decision.
ML System Design 101
This course covers the first layer of ML system design: framing the problem, building data and features, training and evaluating models, serving them, and watching them after launch.
System Design 101
This course introduces the fundamentals of system design through practical examples and common patterns used to draw a first-pass request path in real systems.
System Design Advanced
This course introduces advanced system design through practical examples and common patterns used once a first-pass design already has a request path and a primary store.