AIops Overview
Workflow Automation
LLM-Assisted Incident Response
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
name: llm-incident-response
spec:
entrypoint: analyze-incident
templates:
- name: analyze-incident
steps:
- - name: collect-logs
template: gather-logs
- - name: analyze
template: llm-analysis
- - name: suggest-remediation
template: generate-fix
- name: llm-analysis
container:
image: aiops-toolkit:latest
command: [python, analyze.py]
env:
- name: OPENAI_API_KEY
valueFrom:
secretKeyRef:
name: llm-secrets
key: api-key
Predictive Analytics
Infrastructure Scaling
from openai import OpenAI
from prometheus_api_client import PrometheusConnect
def predict_scaling_needs(metrics_data):
client = OpenAI()
response = client.chat.completions.create(
model="gpt-4",
messages=[
{
"role": "system",
"content": "Analyze infrastructure metrics and recommend scaling actions."
},
{
"role": "user",
"content": f"Metrics data: {metrics_data}"
}
]
)
return response.choices[0].message.content
Code Quality Enhancement
LLM-Powered Code Review
name: LLM Code Review
on: [pull_request]
jobs:
review:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Code Review
uses: coderabbitai/ai-pr-reviewer@latest
with:
github-token: ${{ secrets.GITHUB_TOKEN }}
openai-api-key: ${{ secrets.OPENAI_API_KEY }}
Security Analysis
Threat Detection
ML-based anomaly detection
Pattern recognition
Behavioral analysis
Automated response
Vulnerability Assessment
Code scanning
Dependency analysis
Configuration review
Risk scoring
Performance Optimization
Resource Management
Predictive scaling
Cost optimization
Workload placement
Capacity planning
Monitoring Enhancement
Anomaly detection
Root cause analysis
Alert correlation
Performance prediction
Best Practices
Model Management
Version control
Performance monitoring
Regular updates
Quality assurance
Integration Strategy
Incremental adoption
Fallback mechanisms
Human oversight
Feedback loops
Security Considerations
Data privacy
Model security
Access control
Audit trails
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