- Version: 1.0
- Date: 2024-01-15
- Product Manager: [Your Name]
- Engineering Lead: [Engineering Lead Name]
- Status: Active Development
This document provides a detailed task breakdown for implementing the Loan Eligibility Prediction System based on the PRD requirements. Tasks are organized into epics, sprints, and individual work items with clear acceptance criteria, dependencies, and resource assignments.
- Framework: Agile/Scrum
- Sprint Duration: 2 weeks
- Team Size: 11 members
- Timeline: 12 months (24 sprints)
| Epic ID | Epic Name | Priority | Story Points | Dependencies | Timeline |
|---|---|---|---|---|---|
| E1 | Core Prediction Engine | P0 | 55 | None | Sprints 1-6 |
| E2 | Bias Detection & Fairness | P0 | 34 | E1 | Sprints 4-8 |
| E3 | Model Performance Management | P1 | 29 | E1 | Sprints 6-10 |
| E4 | Integration & Workflow | P0 | 42 | E1 | Sprints 3-9 |
| E5 | Security & Compliance | P0 | 38 | E4 | Sprints 7-12 |
| E6 | Infrastructure & DevOps | P0 | 31 | None | Sprints 1-12 |
| E7 | Testing & Quality Assurance | P0 | 26 | All | Sprints 5-12 |
| E8 | Documentation & Training | P1 | 18 | E1, E4 | Sprints 10-12 |
Total Story Points: 273
Focus: Core ML infrastructure, basic API, security framework
Focus: Banking integrations, advanced features, testing
Focus: Bias detection, performance optimization, pilot deployment
Focus: Enterprise features, monitoring, continuous improvement
Task ID: ML-001
Priority: P0
Story Points: 5
Assignee: Senior ML Engineer
Sprint: 1
User Story: As a data scientist, I need to understand the loan dataset characteristics so that I can design appropriate ML models.
Acceptance Criteria:
- Complete exploratory data analysis (EDA) report
- Statistical summary of all features
- Missing data analysis and patterns
- Feature correlation analysis
- Target variable distribution analysis
- Data quality assessment report
Technical Tasks:
- Load and examine dataset structure
- Generate descriptive statistics
- Create data visualization dashboard
- Identify missing value patterns
- Perform correlation analysis
- Document data insights
Dependencies: None
Definition of Done: EDA report approved by ML team lead, data insights documented
Task ID: ML-002
Priority: P0
Story Points: 8
Assignee: ML Engineer
Sprint: 2
User Story: As an ML engineer, I need automated feature engineering so that models receive optimal input features.
Acceptance Criteria:
- Automated feature creation pipeline
- Handle categorical encoding (one-hot, target encoding)
- Numerical feature scaling and normalization
- Feature interaction creation
- Dimensionality reduction implementation
- Pipeline unit tests with 90%+ coverage
Technical Tasks:
- Implement categorical encoding functions
- Create numerical preprocessing pipeline
- Build feature interaction generator
- Add dimensionality reduction (PCA, feature selection)
- Create feature pipeline configuration
- Write comprehensive unit tests
Dependencies: ML-001
Definition of Done: Feature pipeline processes test data successfully, all tests passing
Task ID: ML-003
Priority: P0
Story Points: 10
Assignee: Senior ML Engineer
Sprint: 2-3
User Story: As an ML engineer, I need scalable model training infrastructure so that I can train multiple models efficiently.
Acceptance Criteria:
- Support for multiple ML algorithms (Random Forest, XGBoost, Neural Networks)
- Cross-validation framework implementation
- Hyperparameter tuning with Optuna/GridSearch
- Model versioning and artifact storage
- Training progress monitoring
- Automated model evaluation metrics
Technical Tasks:
- Implement base model training class
- Add Random Forest model implementation
- Add XGBoost model implementation
- Add Neural Network model implementation
- Create hyperparameter tuning framework
- Implement model versioning system
- Add training monitoring and logging
Dependencies: ML-002
Definition of Done: Can train all model types with hyperparameter tuning, achieving >85% accuracy on test set
Task ID: ML-004
Priority: P0
Story Points: 7
Assignee: ML Engineer
Sprint: 3-4
User Story: As an ML engineer, I need ensemble methods so that I can achieve maximum prediction accuracy.
Acceptance Criteria:
- Voting classifier implementation
- Stacking ensemble with meta-learner
- Blending ensemble techniques
- Ensemble weight optimization
- Model performance comparison framework
- Achieve ≥90% accuracy on test dataset
Technical Tasks:
- Implement voting ensemble
- Create stacking ensemble with cross-validation
- Add weighted ensemble blending
- Build ensemble weight optimization
- Create model comparison utilities
- Performance benchmarking suite
Dependencies: ML-003
Definition of Done: Ensemble model achieves ≥90% accuracy, outperforms individual models
Task ID: ML-005
Priority: P1
Story Points: 5
Assignee: ML Engineer
Sprint: 4
User Story: As a loan officer, I want prediction confidence scores so that I can understand prediction reliability.
Acceptance Criteria:
- Confidence score calculation for all predictions
- Calibrated probability outputs
- Uncertainty quantification methods
- Confidence threshold recommendations
- Score interpretation documentation
Technical Tasks:
- Implement prediction confidence calculation
- Add probability calibration (Platt scaling, isotonic)
- Create uncertainty quantification methods
- Build confidence threshold analysis
- Document score interpretation guidelines
Dependencies: ML-004
Definition of Done: All predictions include confidence scores, calibration validated on test set
Task ID: DP-001
Priority: P0
Story Points: 6
Assignee: Backend Developer
Sprint: 1-2
User Story: As a system admin, I need data validation so that only clean data enters the ML pipeline.
Acceptance Criteria:
- Schema validation for input data
- Data type checking and conversion
- Range validation for numerical fields
- Required field validation
- Format validation (email, phone, etc.)
- Custom business rule validation
Technical Tasks:
- Create data schema definitions
- Implement validation decorators
- Add data type conversion utilities
- Build range and format validators
- Create business rule validation engine
- Add validation error reporting
Dependencies: None
Definition of Done: Validation framework rejects invalid data with detailed error messages
Task ID: DP-002
Priority: P0
Story Points: 7
Assignee: ML Engineer
Sprint: 2-3
User Story: As an ML engineer, I need automated data cleaning so that models receive high-quality input data.
Acceptance Criteria:
- Missing value imputation strategies
- Outlier detection and handling
- Data normalization and standardization
- Duplicate record handling
- Data quality scoring
- Preprocessing pipeline configuration
Technical Tasks:
- Implement missing value imputation (mean, median, mode, KNN)
- Add outlier detection (IQR, Z-score, isolation forest)
- Create normalization utilities
- Build duplicate detection logic
- Implement data quality metrics
- Create configurable preprocessing pipeline
Dependencies: DP-001
Definition of Done: Preprocessing pipeline handles all data quality issues, improves model performance
Task ID: DP-003
Priority: P0
Story Points: 8
Assignee: Backend Developer
Sprint: 3-4
User Story: As a loan officer, I need real-time data processing so that I can get instant eligibility predictions.
Acceptance Criteria:
- Real-time data ingestion API
- Stream processing with <2 second latency
- Batch processing for bulk uploads
- Error handling and retry mechanisms
- Processing status monitoring
- Scalable to 10,000+ requests/hour
Technical Tasks:
- Implement real-time data ingestion endpoint
- Create stream processing pipeline
- Add batch processing capabilities
- Build error handling and retry logic
- Implement processing status tracking
- Add performance monitoring and alerts
Dependencies: DP-002
Definition of Done: Can process real-time requests <2 seconds, batch uploads of 1000+ records
Task ID: DP-004
Priority: P1
Story Points: 6
Assignee: Backend Developer
Sprint: 4-5
User Story: As a data engineer, I need data quality monitoring so that I can detect data issues proactively.
Acceptance Criteria:
- Real-time data quality metrics dashboard
- Data drift detection algorithms
- Quality threshold alerting
- Historical quality trend analysis
- Automated quality reporting
- Integration with monitoring systems
Technical Tasks:
- Implement data quality metrics calculation
- Create data drift detection algorithms
- Build quality threshold monitoring
- Add historical trend analysis
- Create automated reporting system
- Integrate with Prometheus/Grafana
Dependencies: DP-003
Definition of Done: Quality monitoring dashboard operational, alerts configured for data issues
Task ID: FAIR-001
Priority: P0
Story Points: 8
Assignee: Senior ML Engineer
Sprint: 4-5
User Story: As a compliance officer, I need fairness metrics so that I can ensure equitable lending decisions.
Acceptance Criteria:
- Demographic parity calculation
- Equalized odds implementation
- Statistical parity testing
- Disparate impact analysis
- Group fairness metrics
- Individual fairness measures
Technical Tasks:
- Implement demographic parity calculation
- Add equalized odds measurement
- Create statistical parity tests
- Build disparate impact analysis
- Add calibration across groups
- Create fairness metric dashboard
Dependencies: ML-004
Definition of Done: All fairness metrics calculated, demographic parity <5% across groups
Task ID: FAIR-002
Priority: P0
Story Points: 9
Assignee: ML Engineer
Sprint: 5-6
User Story: As a risk manager, I need bias detection so that I can identify unfair model behavior.
Acceptance Criteria:
- Statistical bias tests implementation
- Protected attribute analysis
- Intersectional bias detection
- Temporal bias monitoring
- Automated bias alerting system
- Bias severity scoring
Technical Tasks:
- Implement statistical bias detection tests
- Create protected attribute analysis framework
- Add intersectional bias detection
- Build temporal bias monitoring
- Create automated alerting system
- Implement bias severity scoring
Dependencies: FAIR-001
Definition of Done: Bias detection identifies unfair patterns, alerts trigger appropriately
Task ID: FAIR-003
Priority: P0
Story Points: 10
Assignee: Senior ML Engineer
Sprint: 6-7
User Story: As an ML engineer, I need bias mitigation so that I can create fair prediction models.
Acceptance Criteria:
- Pre-processing bias mitigation (resampling, reweighting)
- In-processing fairness constraints
- Post-processing calibration techniques
- Adversarial debiasing implementation
- Fair representation learning
- Mitigation effectiveness validation
Technical Tasks:
- Implement data resampling techniques
- Add fairness constraint optimization
- Create post-processing calibration
- Build adversarial debiasing network
- Add fair representation learning
- Create mitigation validation framework
Dependencies: FAIR-002
Definition of Done: Bias mitigation reduces unfairness while maintaining accuracy ≥90%
Task ID: API-001
Priority: P0
Story Points: 10
Assignee: Backend Developer
Sprint: 3-4
User Story: As a developer, I need REST APIs so that banking systems can integrate with the prediction engine.
Acceptance Criteria:
- RESTful API endpoints for all core functions
- OpenAPI/Swagger documentation
- Request/response validation
- Rate limiting and throttling
- API versioning support
- Response time <500ms (95th percentile)
Technical Tasks:
- Design API endpoints and schemas
- Implement prediction API endpoints
- Add model management endpoints
- Create API documentation (OpenAPI)
- Implement rate limiting
- Add API versioning support
- Create comprehensive API tests
Dependencies: ML-004
Definition of Done: API endpoints functional with <500ms response time, documentation complete
Task ID: API-002
Priority: P0
Story Points: 7
Assignee: Backend Developer
Sprint: 4-5
User Story: As a security officer, I need secure API access so that only authorized systems can access predictions.
Acceptance Criteria:
- OAuth 2.0 authentication implementation
- JWT token management
- Role-based access controls (RBAC)
- API key management
- Session management
- Audit logging for access
Technical Tasks:
- Implement OAuth 2.0 flow
- Create JWT token service
- Build RBAC system
- Add API key management
- Implement session handling
- Create audit logging system
Dependencies: API-001
Definition of Done: Secure authentication working, role-based access enforced
Task ID: UI-001
Priority: P1
Story Points: 8
Assignee: Frontend Developer
Sprint: 3-4
User Story: As a loan officer, I need an intuitive interface so that I can efficiently process applications.
Acceptance Criteria:
- React.js application framework
- Material-UI component library
- Responsive design (mobile/tablet/desktop)
- Navigation and routing system
- State management (Redux/Context)
- Page load times <3 seconds
Technical Tasks:
- Set up React.js project structure
- Integrate Material-UI components
- Implement responsive design system
- Create navigation and routing
- Set up state management
- Optimize performance and loading
Dependencies: None
Definition of Done: UI framework operational, responsive across devices, <3 second load times
Task ID: UI-002
Priority: P1
Story Points: 9
Assignee: Frontend Developer
Sprint: 5-6
User Story: As a loan officer, I need a prediction dashboard so that I can view and manage loan predictions efficiently.
Acceptance Criteria:
- Real-time prediction display
- Application status tracking
- Batch prediction management
- Filter and search functionality
- Export capabilities (CSV, PDF)
- Prediction confidence visualization
Technical Tasks:
- Design dashboard layout and components
- Implement real-time prediction display
- Add application status tracking
- Create batch prediction interface
- Build filter and search functionality
- Add export capabilities
- Create prediction confidence charts
Dependencies: UI-001, API-001
Definition of Done: Dashboard displays predictions with real-time updates, export functionality working
Task ID: INFRA-001
Priority: P0
Story Points: 10
Assignee: DevOps Engineer
Sprint: 1-2
User Story: As a DevOps engineer, I need cloud infrastructure so that the application can be deployed scalably and reliably.
Acceptance Criteria:
- Multi-environment setup (dev, staging, prod)
- Auto-scaling capabilities
- Load balancing configuration
- Network security groups
- Backup and disaster recovery
- Infrastructure as Code (Terraform)
Technical Tasks:
- Set up AWS/Azure cloud accounts
- Create multi-environment infrastructure
- Configure auto-scaling groups
- Set up load balancers
- Configure network security
- Implement backup strategies
- Create Terraform configurations
Dependencies: None
Definition of Done: All environments operational with auto-scaling and security configured
Task ID: INFRA-002
Priority: P0
Story Points: 8
Assignee: DevOps Engineer
Sprint: 3-5
User Story: As a developer, I need automated deployment so that code changes can be deployed quickly and reliably.
Acceptance Criteria:
- Git-based CI/CD workflow
- Automated testing integration
- Blue-green deployment strategy
- Rollback capabilities
- Deployment approvals
- Artifact management
Technical Tasks:
- Set up CI/CD pipeline (Jenkins/GitLab CI)
- Integrate automated testing
- Implement blue-green deployment
- Add rollback mechanisms
- Create approval workflows
- Set up artifact registry
Dependencies: INFRA-001
Definition of Done: CI/CD pipeline deploys code automatically with testing and rollback capabilities
Task ID: TEST-001
Priority: P0
Story Points: 6
Assignee: QA Engineer + Developers
Sprint: 5-6
User Story: As a developer, I need comprehensive unit tests so that code quality is maintained and regressions are prevented.
Acceptance Criteria:
- 90%+ code coverage for all modules
- Automated test execution in CI/CD
- Test result reporting
- Mocking and stubbing framework
- Performance test benchmarks
- Test data management
Technical Tasks:
- Set up pytest framework for Python
- Create Jest setup for JavaScript
- Implement code coverage reporting
- Add mocking frameworks
- Create performance benchmarks
- Set up test data fixtures
Dependencies: INFRA-002
Definition of Done: 90%+ code coverage achieved, tests run automatically in CI/CD
Task ID: TEST-002
Priority: P0
Story Points: 8
Assignee: QA Engineer
Sprint: 6-7
User Story: As a QA engineer, I need integration tests so that system components work correctly together.
Acceptance Criteria:
- API integration tests
- Database integration tests
- External service integration tests
- End-to-end workflow tests
- Error handling and recovery tests
- Performance and load testing
Technical Tasks:
- Create API integration test suite
- Build database integration tests
- Add external service mock tests
- Implement end-to-end tests
- Create error scenario tests
- Set up load testing framework
Dependencies: TEST-001, API-001
Definition of Done: Integration tests cover all major workflows and error scenarios
| Role | Epic 1 | Epic 2 | Epic 3 | Epic 4 | Epic 5 | Epic 6 | Total |
|---|---|---|---|---|---|---|---|
| Senior ML Engineer | 18 | 17 | 0 | 0 | 0 | 0 | 35 |
| ML Engineer | 20 | 9 | 0 | 0 | 0 | 0 | 29 |
| Backend Developer | 7 | 0 | 35 | 0 | 0 | 0 | 42 |
| Frontend Developer | 0 | 0 | 0 | 24 | 0 | 0 | 24 |
| DevOps Engineer | 0 | 0 | 0 | 0 | 18 | 0 | 18 |
| QA Engineer | 0 | 0 | 0 | 0 | 0 | 14 | 14 |
Sprint Goal: Establish basic infrastructure and begin ML development
Stories: ML-001 (5), DP-001 (6), INFRA-001 (10)
Total Points: 21
Key Deliverables:
- EDA report completed
- Data validation framework operational
- Cloud infrastructure provisioned
Sprint Goal: Complete data processing and continue infrastructure setup
Stories: ML-002 (8), ML-003 (10), DP-002 (7)
Total Points: 25
Key Deliverables:
- Feature engineering pipeline complete
- Model training infrastructure ready
- Data preprocessing operational
Sprint Goal: Complete core ML models and begin API development
Stories: ML-003 (cont), ML-004 (7), DP-003 (8), API-001 (10), UI-001 (8)
Total Points: 33
Key Deliverables:
- Ensemble models achieving 90%+ accuracy
- REST API endpoints operational
- UI framework established
Sprint Goal: Enhance ML capabilities and continue system integration
Stories: ML-005 (5), DP-004 (6), API-001 (cont), API-002 (7), FAIR-001 (8)
Total Points: 26
Key Deliverables:
- Prediction confidence scoring implemented
- API authentication secured
- Fairness metrics framework established
Sprint Goal: Implement bias detection and establish testing framework
Stories: FAIR-001 (cont), FAIR-002 (9), UI-002 (9), TEST-001 (6), INFRA-002 (8)
Total Points: 32
Key Deliverables:
- Bias detection algorithms operational
- Prediction dashboard operational
- Testing framework established
Sprint Goal: Complete core features and comprehensive testing
Stories: FAIR-003 (10), UI-002 (cont), TEST-001 (cont), TEST-002 (8)
Total Points: 18
Key Deliverables:
- Bias mitigation techniques implemented
- Unit testing framework complete
- Integration testing operational
- ML-001 → ML-002 → ML-003 → ML-004 (Core ML Pipeline)
- INFRA-001 → INFRA-002 (Infrastructure)
- API-001 → API-002 (API Development)
- ML-004 → FAIR-001 → FAIR-002 → FAIR-003 (Fairness Implementation)
- Story points completed vs. planned
- Bug count and severity distribution
- Code coverage percentage
- System performance benchmarks
- Sprint goal achievement rate
- Velocity tracking and trends
- Technical debt accumulation
- User feedback scores
- Code review completed by senior team member
- Unit tests written with ≥90% coverage
- Integration tests passing
- Security scan passed
- Performance benchmarks met
- Documentation updated
- Acceptance criteria met
- User story validated
- Business logic verified
- Error handling implemented
- Logging and monitoring added
- Accessibility compliance checked
- Feature flagged and configurable
- Rollback procedure documented
- Monitoring alerts configured
- Support documentation updated
- Training materials prepared
- Go/no-go criteria evaluated
This task breakdown document serves as the primary reference for development activities and should be updated regularly to reflect progress and changes.