Build Production-Ready AI
From LLMs to deployment—master the patterns, tools, and techniques that power real-world AI applications
Fundamentals of LLM
A comprehensive deep dive into the mathematical and computational bedrock of Large Language Models. Master the linear algebra, calculus, and probability theory that power attention mechanisms. Build a transformer from scratch using Python and PyTorch, understanding every matrix multiplication and gradient flow.
Software Systems Engineering
Bridge the gap between theory and production. Learn to architect scalable, fault-tolerant distributed systems. From advanced database design and microservices patterns to CI/CD pipelines and observability, this course prepares you to build the infrastructure that powers modern AI applications.
Generative AI Engineering
The definitive guide to building with GenAI. Move beyond simple prompts to mastering RAG (Retrieval-Augmented Generation), vector databases, and agentic workflows. Learn to evaluate, fine-tune, and deploy LLMs in production environments, handling latency, cost, and quality tradeoffs.
Forward Deployment Decisions
Develop the strategic intuition of a Principal Engineer. Learn a rigorous framework for making high-stakes technical decisions. Analyze tradeoffs between build vs. buy, monolithic vs. microservices, and selecting the right tech stack for web apps, mobile, and AI-driven backends.
Product Management Fundamentals
Master the art of building products users love. Learn to map user journeys, define MVPs, and prioritize features effectively. Understand the unique challenges of AI product management, from managing probabilistic outcomes to setting user expectations and measuring success.
AI Ethics, Compliance & Governance
Navigate the complex landscape of responsible AI. Learn to build systems that are fair, transparent, and compliant with global regulations (EU AI Act, NIST). Master techniques for bias detection, red-teaming, and implementing robust governance frameworks for enterprise AI.