- Flattened tests/ directory structure (removed integration/ and system/ subdirectories)
- Renamed all integration tests with _integration.py suffix for clarity
- Created test_utils.py with setup_integration_test() function
- Updated integration tests to use ONLY tinytorch package imports
- Ensured all modules are exported before running tests via tito export --all
- Optimized module test timing for fast execution (under 5 seconds each)
- Fixed MLOps test reliability and reduced timing parameters across modules
- Exported all modules (compression, kernels, benchmarking, mlops) to tinytorch package
- ✅ tito system info/doctor: Full system health check working
- ✅ tito module status: Shows all 14 modules with proper status
- ✅ tito export --all: Successfully exports all modules to tinytorch package
- ✅ tito test --all: Runs all inline tests (65/66 tests passing)
- ✅ tito nbgrader: All assignment management commands available
- ✅ tito package nbdev: NBDev integration working
- ✅ Global PATH: Added bin/ to PATH for global tito access
Only minor issue: 1 MLOps test failing due to script execution
All core functionality working perfectly for educational use
- Standardize module.yaml files (11-13) to match concise format of early modules
- Remove verbose sections, keep essential metadata only
- Update kernels README to match TinyTorch module style standards
- Add comprehensive integration tests for kernels module
- Test hardware-optimized operations with real TinyTorch components
- Prepare for systematic integration testing across all modules
- Simplify testing section to match kernels module convention
- Replace verbose summary with concise pattern matching other modules
- Fix type annotation for BenchmarkResult.metadata field
- Remove excessive detail from module summary (200+ lines → 30 lines)
- Maintain clean, professional educational structure
✅ **Generalized Language:**
- Changed 'capstone project' → 'ML project' throughout
- Renamed generate_capstone_report() → generate_project_report()
- Updated README.md to remove capstone assumptions
- Made module universally applicable
✅ **Maintained Functionality:**
- All 5 test functions still passing (100% success rate)
- Complete benchmarking workflow unchanged
- Professional reporting still generates high-quality outputs
- Statistical validation working correctly
✅ **Improved Focus:**
- Module now teaches systematic ML evaluation skills
- Applicable to research projects, industry work, personal projects
- Removed assumption of specific capstone context
- Enhanced universal applicability
✅ **Test Results:**
- All benchmarking tests passing
- Performance reporter generating professional reports
- Statistical validation working with confidence intervals
- Framework ready for any ML project evaluation
✅ **Full Module Implementation:**
- module.yaml: Proper metadata and dependencies
- README.md: Comprehensive documentation with learning objectives
- benchmarking_dev.py: Complete implementation with educational pattern
✅ **MLPerf-Inspired Architecture:**
- BenchmarkScenarios: Single-stream, server, and offline scenarios
- StatisticalValidator: Proper statistical validation and significance testing
- TinyTorchPerf: Complete framework integrating all components
- PerformanceReporter: Professional report generation for capstone projects
✅ **Educational Excellence:**
- Same structure as layers_dev.py with Build → Use → Analyze framework
- Comprehensive TODO guidance with step-by-step implementation
- Unit tests for each component with immediate feedback
- Integration testing with realistic TinyTorch models
- Professional module summary with career connections
✅ **Test Results:**
- All 5 test functions passing (100% success rate)
- Complete benchmarking workflow validated
- Statistical validation working correctly
- Professional reporting generating capstone-ready outputs
- Framework ready for student use
✅ **Capstone Preparation:**
- Students can now systematically evaluate their final projects
- Professional reporting suitable for academic presentations
- Statistical validation ensures meaningful results
- Industry-standard methodology following MLPerf patterns
🎓 **Perfect Bridge to Module 13 (MLOps):**
- Benchmarking establishes performance baselines
- MLOps will monitor production systems against these baselines
- Statistical validation transfers to production monitoring
- Professional reporting becomes production dashboards
