Build production AI systems — retrieval-augmented generation, autonomous agents, evaluation, interpretability, safety, and inference optimization. Bridge the gap between research papers and shipped products.

Master the building blocks of cloud-native systems — compute, storage, networking, serverless, containers, IaC, CI/CD, and multi-region architecture.

Understand the internals of data systems — storage engines, replication, partitioning, transactions, batch processing, and stream processing. Based on the concepts from Martin Kleppmann's "Designing Data-Intensive Applications."

Master algorithmic patterns and data structures through hands-on LeetCode-style problems - from arrays and hashing to dynamic programming and advanced graphs.

Understand how AI agents work, from LLM foundations to production-ready agent architectures.

Learn how machine learning systems are built, deployed, and operated at scale — from feature engineering to model serving to monitoring.

Master object-oriented design from first principles, SOLID, design patterns, and classic interview problems with hands-on coding.

Build a strong foundation in designing scalable, reliable distributed systems.

A short course that equips you with the skills to approach system design interviews methodically.