- 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
- Shortened verbose 119-line summary to focused 32-line format
- Removed redundant sections and excessive congratulatory language
- Added standard Next Steps with actionable tito commands
- Now consistent with other module endings (tensor, layers, optimizers, etc.)
- Maintains essential accomplishments and real-world connections
- Update MLOps module ending to match standard TinyTorch module format
- Remove verbose ending text, use concise professional summary
- Add comprehensive benchmarking integration tests
- Test benchmarking framework with real TinyTorch components
- Include tests for kernels, networks, and statistical validation
- Follow established integration test patterns
- Replace overly celebratory ending with standard progress indicator
- Use same format as other modules: 'Final Progress: [module] ready for [next step]!'
- Maintain professional, educational tone consistent with project
- Complete MLOps pipeline with 4 core components:
1. ModelMonitor: Tracks performance over time, detects degradation
2. DriftDetector: Statistical tests for data distribution changes
3. RetrainingTrigger: Automated retraining based on thresholds
4. MLOpsPipeline: Orchestrates complete workflow integration
- Follows TinyTorch educational pattern exactly:
- Concept explanations before implementation
- Guided TODOs with step-by-step instructions
- Immediate testing after each component
- Progressive complexity building on previous modules
- Comprehensive summary with career applications
- Integrates all previous TinyTorch components:
- Uses training pipeline from Module 09
- Uses benchmarking from Module 12
- Uses compression from Module 10
- Demonstrates complete ecosystem integration
- Production-ready MLOps concepts:
- Performance monitoring and alerting
- Drift detection with statistical validation
- Automated retraining triggers
- Model lifecycle management
- Complete deployment workflows
- Educational value:
- Real-world MLOps applications (Netflix, Uber, Google)
- Industry connections (MLflow, Kubeflow, SageMaker)
- Career preparation for ML Engineer roles
- Complete capstone bringing together all 13 modules
- Technical implementation:
- 1700+ lines of educational content and code
- NBGrader integration for assessment
- Comprehensive test suite with 100+ points
- Auto-discovery testing framework
- Professional documentation and examples
This completes the TinyTorch ecosystem with production-ready MLOps
