Performance & Tools
Model Optimization
| Technique | Description |
|---|---|
| Quantization | Reduce model size and inference time |
| Pruning | Remove unnecessary model parameters |
| Distillation | Train smaller models from larger ones |
| Caching | Cache frequent predictions |
Infrastructure Optimization
| Technique | Description |
|---|---|
| GPU Acceleration | Use GPUs for faster inference |
| Load Balancing | Distribute requests across instances |
| Auto-scaling | Scale resources based on demand |
| CDN | Cache model artifacts closer to users |
Data Science Tools
Development Environment
- Jupyter Notebooks - Interactive model development
- BigQuery - Large-scale data analysis
- MLflow - Model lifecycle management
- DVC - Data version control
Monitoring Tools
- Metabase - Model performance dashboards
- Streamlit - Interactive model demos
- Grafana - System monitoring and alerting
Best Practices
Model Development
- Data Validation - Always validate input data
- Feature Engineering - Create meaningful features
- Cross-validation - Use proper validation techniques
- Documentation - Document model assumptions and limitations
Deployment
- Version Control - Track all model versions
- Testing - Test models thoroughly before deployment
- Monitoring - Monitor model performance continuously
- Rollback - Have rollback procedures ready