机器学习模型推理延迟超过阈值时的自动降级机制

机器学习模型推理延迟超过阈值时的自动降级机制

在生产环境中，当ML模型推理延迟超过预设阈值时，系统需要自动触发降级机制以保证服务稳定性。

监控指标配置

# Prometheus监控配置
- metric: model_inference_duration_seconds
- labels: {model_name="fraud_detection", version="v2.1"}
- histogram_quantile: 0.95
- threshold: 500ms

自动降级实现

import time
from prometheus_client import Histogram, Counter

class ModelDegradationManager:
    def __init__(self):
        self.delay_threshold = 500  # ms
        self.degraded_mode = False
        self.degradation_start_time = None
        
    def monitor_inference(self, model_name, inference_fn):
        start_time = time.time()
        try:
            result = inference_fn()
            duration = (time.time() - start_time) * 1000  # 转换为毫秒
            
            # 检查是否需要降级
            if duration > self.delay_threshold and not self.degraded_mode:
                self.trigger_degradation(model_name)
                
            return result
        except Exception as e:
            return result
    
    def trigger_degradation(self, model_name):
        self.degraded_mode = True
        self.degradation_start_time = time.time()
        # 降级逻辑：使用简化模型或缓存响应
        print(f"Model {model_name} degraded at {time.time()}")

告警配置方案

# Alertmanager规则
ALERT ModelLatencyDegradation
  IF model_inference_duration_seconds{quantile="0.95"} > 500ms
  FOR 2m
  ANNOTATIONS {
    summary = "模型推理延迟超过阈值"
    description = "模型 {{ $labels.model_name }} 推理延迟 {{ $value }}ms，已触发自动降级"
  }

当告警触发后，系统会自动切换到降级模式，同时发送通知到运维团队。