AIOps Implementation Guide (2024+)

Automated Incident Management

AWS Implementation

AWSTemplateFormatVersion: '2010-09-09'
Resources:
  LLMFunction:
    Type: AWS::Lambda::Function
    Properties:
      Handler: index.handler
      Runtime: python3.11
      Code:
        ZipFile: |
          import boto3
          import openai
          
          def analyze_cloudwatch_logs(log_data):
              response = openai.ChatCompletion.create(
                  model="gpt-4",
                  messages=[
                      {"role": "system", "content": "Analyze CloudWatch logs and suggest remediation steps."},
                      {"role": "user", "content": f"Logs: {log_data}"}
                  ]
              )
              return response.choices[0].message.content

## Azure Integration

### Cognitive Services Setup
```yaml
resource "azurerm_cognitive_account" "aiops" {
  name                = "aiops-cognitive"
  location            = azurerm_resource_group.main.location
  resource_group_name = azurerm_resource_group.main.name
  kind                = "CognitiveServices"
  sku_name            = "S0"

  custom_subdomain_name = "aiops-analysis"
  network_acls {
    default_action = "Deny"
    ip_rules       = ["10.0.0.0/16"]
  }
}

Predictive Scaling

Kubernetes HPA with AI

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: ai-powered-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: my-app
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: External
    external:
      metric:
        name: aiops_prediction
        selector:
          matchLabels:
            metric_name: load_prediction
      target:
        type: AverageValue
        averageValue: "50"

Best Practices

1. Data Collection
   • Standardized logging
   • Metric aggregation
   • Trace correlation
   • Event categorization
2. Model Management
   • Version control
   • A/B testing
   • Performance monitoring
   • Retraining pipelines
3. Integration Points
   • Alerting systems
   • ITSM platforms
   • CI/CD pipelines
   • Monitoring tools
4. Governance
   • Model validation
   • Access control
   • Audit logging
   • Compliance checks