Building an AI product is only half the battle—operating it reliably at scale is where most teams stumble. Unlike traditional software where bugs are deterministic and reproducible, AI systems can fail in subtle, probabilistic ways that erode user trust before you even notice.
123456789101112from fastapi import FastAPI, HTTPException from datetime import datetime, timedelta import numpy as np from dataclasses import dataclass from typing import Dict, List, Optional import asyncio app = FastAPI() @dataclass class HealthStatus: status: str # healthy, degraded, critical
123456789101112from dataclasses import dataclass from typing import Callable, Dict, Optional import hashlib from datetime import datetime, timedelta import asyncio @dataclass class ExperimentConfig: name: str control_model: str treatment_model: str traffic_percentage: float # 0-100
123456789101112from datetime import datetime, timedelta from typing import Dict, Optional import asyncio from enum import Enum class CostAlertLevel(Enum): NORMAL = 'normal' WARNING = 'warning' CRITICAL = 'critical' EMERGENCY = 'emergency' class AICostController: