模型部署自动化流程优化
核心监控指标配置
模型性能指标：
- 推理延迟：设置P95延迟超过200ms触发告警
- 准确率下降：当准确率连续3次采样下降超过0.02触发告警
- 内存使用率：内存占用超过85%时告警
自动化部署脚本
#!/bin/bash
# 部署脚本
MODEL_NAME=$1
DEPLOY_ENV=$2
echo "开始部署模型 $MODEL_NAME 到环境 $DEPLOY_ENV"
# 1. 模型验证
if ! python -m model_validator.validate --model-path ./models/$MODEL_NAME; then
echo "模型验证失败" >&2
exit 1
fi
# 2. 部署到K8s
kubectl set image deployment/$MODEL_NAME-deployment model=$MODEL_NAME:latest
# 3. 健康检查
sleep 30
if ! kubectl rollout status deployment/$MODEL_NAME-deployment; then
echo "部署失败，回滚到上一版本"
kubectl rollout undo deployment/$MODEL_NAME-deployment
exit 1
fi
# 4. 监控配置
kubectl apply -f monitoring/config-$DEPLOY_ENV.yaml
echo "部署完成，监控已启动"
告警配置方案
Prometheus告警规则：
- alert: ModelLatencyHigh
expr: histogram_quantile(0.95, sum(rate(model_inference_duration_seconds_bucket[5m])) by (job)) > 0.2
for: 3m
labels:
severity: warning
annotations:
summary: "模型延迟过高"
CI/CD流水线集成：
- 部署后自动运行性能测试套件
- 失败时自动触发回滚机制
- 配置Slack通知通道
讨论