Machine Learning Project Lifecycle

Problem Definition

Business Understanding

• Problem Identification
• Goal Setting
• Success Metrics
• Constraints and Requirements

Project Planning

• Timeline and Milestones
• Resource Allocation
• Team Roles
• Risk Assessment

Data Strategy

• Data Sources
• Data Collection Methods
• Data Quality Requirements
• Legal and Privacy Considerations

Data Collection and Preparation

Data Collection

• Data Gathering
• Data Storage
• Data Versioning
• Data Documentation

Data Exploration

• Statistical Analysis
• Data Visualization
• Pattern Discovery
• Anomaly Detection

Data Preprocessing

• Data Cleaning
• Feature Engineering
• Data Transformation
• Data Validation

Model Development

Model Selection

• Algorithm Choice
• Model Architecture
• Baseline Models
• Evaluation Criteria

Model Training

• Training Pipeline
• Hyperparameter Tuning
• Cross-validation
• Model Iteration

Model Evaluation

• Performance Metrics
• Error Analysis
• Model Comparison
• Validation Strategy

Deployment

Production Preparation

• Model Optimization
• API Development
• Infrastructure Setup
• Documentation

Testing

• Unit Testing
• Integration Testing
• Load Testing
• User Acceptance Testing

Deployment Strategy

• Deployment Methods
• Rollback Plans
• Monitoring Setup
• Alert Systems

Monitoring and Maintenance

Performance Monitoring

• Model Performance
• System Health
• Data Quality
• Resource Usage

Model Updates

• Retraining Strategy
• Version Control
• A/B Testing
• Model Iteration

Documentation

• Code Documentation
• Model Documentation
• Process Documentation
• Maintenance Guides

Project Closure

Evaluation

• Goal Achievement
• Lessons Learned
• Performance Analysis
• ROI Assessment

Knowledge Transfer

• Team Training
• Documentation Handover
• Support Setup
• Future Recommendations