The AI/ML Development Lifecycle
Phase 1: Problem Definition
What problem are we solving?
Is AI the right solution?
What does success look like?Phase 2: Data Collection & Preparation
1.
Collection: Gather raw data
2.
Cleaning: Handle missing values, outliers
3.
Preprocessing: Normalization, encoding
4.
Splitting: Train/Validation/Test sets
Phase 3: Model Development
Choose baseline model
Feature engineering
Model selection and architecture
Hyperparameter tuningPhase 4: Evaluation
Classification: Accuracy, Precision, Recall, F1
Regression: MSE, MAE, R²
Generation: BLEU, ROUGE, Human evaluationPhase 5: Deployment
REST APIs, Batch processing
Edge deployment, ServerlessPhase 6: Monitoring & Maintenance
Performance degradation
Data drift, Concept drift
Automated retraining