Build production-grade reasoning for AI/ML systems.
Workbook-style courses that teach you to defend tradeoffs, not just memorize tools. Structured around active reasoning — commit before reveal, exercises that mirror real decisions, and case studies that show the tradeoff sequence.
Three audiences. One standard.
ML Engineers
Data Scientists
AI Product Builders
Active reasoning. Not passive reading.
Every course is structured around active reasoning — commit before reveal, exercises that mirror real decisions, and case studies that show the tradeoff sequence — not just the outcome.
- 01Read the chapter
Plain English. Expert perspective. Written by someone who has shipped this in production.
- 02Work through the exercises
Commit your answer before the reveal unlocks. You only build judgment by making a call first.
- 03Apply to a case study
A real decision sequence from a production system. What would you decide? Then see what actually happened and why.
Start here.
Learn how to reason about ML systems in production through structured chapters, decision frameworks, and case-study-driven workbook exercises.
A course for senior engineers and product leaders on what agentic AI actually is, how to evaluate it strategically, and how to go from pilot to production.
A technical workbook for engineers building production AI agents — covering LLM core, memory architectures, tool design, multi-agent systems, evaluation, and production operations.