- Add mandatory ML Systems Thinking Questions section (environment deps, automation, production)
- Add systems analysis with memory/performance profiling
- Add production context (Docker, Kubernetes, CI/CD, dependency management)
- Fix section ordering: main block → ML Systems Thinking → Module Summary (last)
- Add environment resource analysis function with tracemalloc
- Maintain simple first-day setup approach while adding systems depth
- Full compliance with CLAUDE.md and testing standards
Major simplification based on instructor feedback:
- Reduced from complex testing to just 3 simple functions
- setup(): Install packages via pip
- check_versions(): Quick Python/NumPy version check
- get_info(): Basic name and email collection
Changes:
- Removed complex command execution and system profiling
- Removed comprehensive memory and performance testing
- Fixed unused 'os' import
- Streamlined to ~220 lines for perfect first-day experience
Team validated: Simple, welcoming, and gets students ready quickly
Remove complex "5 C's" pedagogical framework and focus on simple environment readiness:
- Remove overly complex CONCEPT/CODE/CONNECTIONS/CONSTRAINTS/CONTEXT structure
- Add verify_environment() function for basic Python/package verification
- Simplify learning goals to focus on environment readiness
- Update content for "first day of class" tone without complex theory
- Fix Python 3.13 typing compatibility issue
- Maintain all core functionality while improving accessibility
Module now serves as welcoming entry point for students to verify their environment works.
All agents signed off: Module Developer, QA, Package Manager, Documentation Review
