监控系统数据聚合分析

监控系统数据聚合分析

在机器学习模型运行时监控中，数据聚合分析是核心环节。以TensorFlow Serving为例，我们需重点关注以下指标：

核心监控指标配置

请求成功率：{"metric": "tensorflow_serving_request_count", "aggregation": "rate(1m)"} 平均延迟：{"metric": "tensorflow_serving_request_duration_ms", "aggregation": "avg()"} 错误率：{"metric": "tensorflow_serving_request_count", "filter": "error", "aggregation": "rate(1m)"}

告警配置方案

# 告警规则配置
rules:
  - name: high_error_rate
    metric: tensorflow_serving_request_count
    condition: >
      rate(5m) > 0.05
    severity: warning
    duration: 5m

  - name: latency_spike
    metric: tensorflow_serving_request_duration_ms
    condition: >
      avg() > 2000
    severity: critical
    duration: 3m

实际操作步骤

1. 配置Prometheus抓取目标：

   curl -X POST http://prometheus:9090/api/v1/alerts
   

2. 创建Grafana仪表板：

   {
     "dashboard": {
       "title": "Model Performance",
       "panels": [
         {"targets": [{"expr": "rate(tensorflow_serving_request_count[5m])"}]}
       ]
     }
   }
   

通过以上配置，可实现对模型性能的实时监控与异常告警。