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AI Model Fundamentals
Training, evaluation, and optimization of AI models
⏱️ Estimated reading time: 22 minutes
ML Model Lifecycle
Phases
1. Data Preparation
- Collection
- Cleaning
- Labeling
- Feature engineering
2. Training
- Algorithm selection
- Train/test split
- Iterative training
3. Evaluation
- Performance metrics
- Cross-validation
- Overfitting/underfitting detection
4. Deployment
- Inference
- Monitoring
- Updates
🎯 Key Points
- ✓ Clear phases: data prep, training, evaluation and deployment
- ✓ Data quality largely determines model success
- ✓ Versioning and reproducibility (model registry, seeds, pipelines) are critical
- ✓ Post-deployment monitoring for data drift and performance degradation
- ✓ Automated pipelines (CI/CD) reduce errors and speed iterations
Model Customization
Fine-Tuning
Adjusting a pre-trained model with domain-specific data.
Advantages:
- Better performance on specific tasks
- Requires less data than training from scratch
- Faster than full training
Disadvantages:
- Requires training data
- Can be expensive
- Risk of overfitting
Prompt Engineering
Designing effective instructions to guide model responses.
Techniques:
- Zero-shot prompting
- Few-shot prompting
- Chain-of-thought
- System prompts
🎯 Key Points
- ✓ Fine-tuning improves task-specific performance but requires labeled data
- ✓ Choose between fine-tuning and prompt engineering based on cost, data and control needs
- ✓ Watch for overfitting: use validation and regularization
- ✓ Prompt engineering is fast and low-cost for many cases but offers less absolute control
- ✓ Assess safety and bias when customizing models
Metrics and Evaluation
Classification Metrics
- Accuracy: Overall precision
- Precision: Correct positives / Total predicted positives
- Recall: Correct positives / Total actual positives
- F1-Score: Harmonic mean of precision and recall
Regression Metrics
- MAE: Mean Absolute Error
- MSE: Mean Squared Error
- RMSE: Root Mean Squared Error
- R²: Coefficient of determination
LLM Evaluation
- BLEU: Sequence similarity
- ROUGE: Text summaries
- Perplexity: Predictive quality
- Human evaluation
🎯 Key Points
- ✓ Choose metrics aligned with business goals (e.g., recall for fraud detection)
- ✓ Account for class imbalance and use robust metrics (precision/recall, AUC)
- ✓ Tune thresholds and calibrate probabilities for operational decisions
- ✓ For LLMs, combine automated metrics with human evaluation and safety testing
- ✓ Monitor metrics in production and review frequently