AI Project Lifecycle Mastery

# Phase gate checklist - Don't proceed without these! class AIProjectLifecycle: def can_exit_discovery(self): checklist = { "problem_defined": True, # Clear problem statement "users_identified": True, # Know your users "data_identified": True, # Data sources located "success_metrics": True, # KPIs defined "business_value": True # ROI estimated } return all(checklist.values()) def can_exit_feasibility(self): checklist = { "data_quality_ok": True, # Sufficient quality "poc_successful": True, # Technical validation "resources_available": True, # Team & budget "risks_acceptable": True, # Risk mitigation plan "timeline_realistic": True # Achievable schedule } return all(checklist.values()) def can_exit_development(self): checklist = { "model_trained": True, # Model ready "accuracy_threshold": True, # Meets requirements "integration_complete": True, # Connected to systems "documentation_done": True, # Documented "code_reviewed": True # Quality assured } return all(checklist.values()) # Example: Check before moving to next phase project = AIProjectLifecycle() if project.can_exit_discovery(): print("✅ Ready for Feasibility phase") else: print("⚠️ Complete Discovery checklist first")

# Progressive complexity approach class IterativeAIDevelopment: def __init__(self): self.versions = [] def mvp_version(self): # Start with simplest possible solution mvp = { "name": "MVP - Rule-based baseline", "timeline": "4 weeks", "complexity": "Low", "data_needed": 1000, # samples "team_size": 2, "goals": [ "Validate problem exists", "Establish baseline metrics", "Get user feedback", "Learn about edge cases" ], "success_criteria": "Better than random" } return mvp def v2_enhanced(self): # Add ML once problem is validated v2 = { "name": "V2 - Basic ML model", "timeline": "8 weeks", "complexity": "Medium", "data_needed": 10000, "team_size": 4, "improvements": [ "Machine learning model", "Feature engineering", "A/B testing framework", "Basic personalization" ], "success_criteria": "20% improvement over MVP" } return v2 def v3_production(self): # Scale and optimize v3 = { "name": "V3 - Production-ready system", "timeline": "12 weeks", "complexity": "High", "data_needed": 100000, "team_size": 8, "enhancements": [ "Deep learning models", "Real-time processing", "Auto-scaling infrastructure", "Continuous learning pipeline" ], "success_criteria": "Meet all KPIs + SLAs" } return v3 # Key insight: Each version builds on learnings from previous # Risk is distributed across iterations, not all upfront

# Advanced: Self-improving AI system class ContinuousLearningPipeline: def __init__(self): self.model_version = "1.0.0" self.performance_threshold = 0.85 self.drift_threshold = 0.05 def monitor_performance(self, predictions, actuals): # Real-time performance tracking current_accuracy = self.calculate_accuracy(predictions, actuals) if current_accuracy < self.performance_threshold: self.trigger_retraining() return { "current_accuracy": current_accuracy, "status": "healthy" if current_accuracy > self.performance_threshold else "degraded" } def detect_data_drift(self, new_data, baseline_data): # Statistical drift detection drift_score = self.calculate_drift(new_data, baseline_data) if drift_score > self.drift_threshold: alert = { "type": "DATA_DRIFT_DETECTED", "severity": "high" if drift_score > 0.1 else "medium", "action": "Consider model retraining" } self.send_alert(alert) def automated_retraining_pipeline(self): # Fully automated retraining stages = [ "collect_new_data", # From production "validate_data_quality", # Quality checks "retrain_model", # New version "validate_performance", # Compare to current "canary_deployment", # Test with 5% traffic "gradual_rollout", # Increase to 100% "update_version" # v1.0.0 → v1.1.0 ] for stage in stages: if not self.execute_stage(stage): self.rollback() break # Key Benefits: # - Model stays current with changing data # - Automatic performance optimization # - Reduced manual intervention # - Faster response to drift