Deliverables

Your assessment consists of two complementary mini-projects designed to demonstrate different aspects of machine learning engineering across diverse domains and technical challenges.

Checklist & Quality Standards

For each project, ensure you submit all components organized by core competency assessment:

[BC04] Software Engineering Best Practices

Git Workflow & Version Control:

• Clean Git repository with atomic commits (one feature/fix per commit)
• Descriptive commit messages following conventional format (feat:, fix:, docs:, refactor:, etc.)
• Logical commit progression showing incremental development story
• Regular commits demonstrating consistent development hygiene
• Final repository state representing production-ready code

Code Quality & Architecture:

• Modular source code with clear separation of concerns (DataProcessor, FeatureEngineer, ModelTrainer, Evaluator)
• Consistent Python naming conventions and PEP 8 style compliance
• Comprehensive docstrings for all classes and methods
• Inline comments explaining complex algorithmic logic
• Appropriate error handling with informative exception messages
• Unit tests for critical functionality and edge cases
• Configuration files and dependency specifications (requirements.txt, setup.py)

[BC07] End-to-End ML Pipeline Design

Pipeline Architecture:

• Complete implementation of all required pipeline components
• Reusable and configurable components enabling different experimental setups
• Parameter-driven design with external configuration management
• Command-line interface for production deployment readiness
• Proper model persistence and artifact management

MLflow Integration & Experiment Management:

• Comprehensive parameter logging for all experiments and hyperparameter tuning
• Complete metrics tracking across different experimental scenarios
• Model registration with proper versioning and artifact storage
• Structured experiment organization enabling systematic performance comparison
• Reproducible experiment configurations with tracked dependencies

[BC02] Data-Driven Communication

Executive Documentation (README.md):

• Clear business problem statement suitable for stakeholder review
• Quantitative key findings with statistical significance analysis
• Actionable recommendations for deployment and production use
• Risk assessment with model limitations and confidence levels
• Professional performance visualizations supporting business narrative

Technical Documentation:

• Detailed methodology explanation with algorithm choice justification
• Comprehensive dataset documentation (sources, characteristics, quality assessment)
• Project structure documentation describing code organization and architecture
• Statistical performance analysis and model comparison methodology
• Complete installation and usage instructions for technical teams
• Author information with clear task distribution (for team projects)

Experiment Artifacts & Analysis:

• Performance analysis plots and statistical validation results
• Cross-validation strategy documentation and results interpretation
• Model comparison analysis with confidence intervals and significance testing
• Feature importance analysis and engineering decisions rationale
• Final repository state representing production-ready code