---
description: Standards for implementing AI-driven DevOps pipelines and automation
globs: ["**/*.{yml,yaml,json,ts,js}", ".github/**/*", "Dockerfile*"]
priority: 15
---
# AI DevOps Pipeline Standards
## Core Components
### Predictive Analytics & Monitoring
- Implement proactive issue detection
- Use ML models for anomaly detection
- Set up comprehensive logging and metrics
### Automated Testing & Code Generation
- Implement AI-driven test case generation
- Use code quality analysis tools
- Automate code review processes
### Self-Healing Systems
- Implement auto-remediation capabilities
- Set up dynamic resource allocation
- Monitor system health metrics
## Code Standards
### Pipeline Configuration
```yaml
# Good: Structured pipeline with AI integration
name: AI-Enhanced CI/CD
on: [push, pull_request]
jobs:
analyze:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: AI Code Review
uses: ai-code-review@v1
with:
model: 'code-review-v1'
test:
needs: analyze
steps:
- name: Generate Tests
uses: ai-test-gen@v1
# Bad: Basic pipeline without intelligence
name: Basic CI
on: push
jobs:
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2 # ❌ Missing intelligence layer
```
### Monitoring Implementation
```typescript
// Good: AI-driven monitoring
class AIPoweredMonitoring {
async detectAnomalies(metrics: Metrics[]): Promise<Alert[]> {
const predictions = await this.model.predict(metrics);
return this.analyzeAnomalies(predictions);
}
private async analyzeAnomalies(predictions: Prediction[]): Promise<Alert[]> {
return predictions
.filter(p => p.confidence > this.threshold)
.map(p => this.createAlert(p));
}
}
// Bad: Simple threshold monitoring
class BasicMonitoring {
checkMetrics(metrics: Metrics[]) {
return metrics.filter(m => m.value > threshold); // ❌ No intelligence
}
}
```
### Resource Management
```typescript
// Good: ML-based resource allocation
class ResourceManager {
async optimizeResources(usage: Usage[]): Promise<Allocation[]> {
const prediction = await this.predictFutureUsage(usage);
return this.allocateBasedOnPrediction(prediction);
}
}
// Bad: Static allocation
class BasicManager {
allocateResources() {
return DEFAULT_ALLOCATION; // ❌ No dynamic adjustment
}
}
```
## Validation Rules
```typescript
const DevOpsRules = {
// Ensure AI integration in pipelines
requireAIIntegration: {
pattern: /uses:\s*.*ai-.*@v\d/,
message: "Pipelines should integrate AI capabilities"
},
// Validate monitoring implementation
monitoringImplementation: {
pattern: /class.*Monitoring.*{[^}]*predict|analyze/,
message: "Monitoring should include predictive capabilities"
},
// Check for dynamic resource management
resourceManagement: {
pattern: /predict.*Usage|optimize.*Resources/,
message: "Resource management should be dynamic and predictive"
}
};
```
## Security Considerations
1. Model Security
- Validate ML model inputs and outputs
- Monitor for model drift
- Implement model versioning
2. Pipeline Security
- Secure CI/CD configurations
- Implement least privilege access
- Scan for security vulnerabilities
3. Resource Protection
- Implement resource quotas
- Monitor resource usage patterns
- Set up abuse detection
## Infrastructure Requirements
1. Containerization
- Use secure base images
- Implement multi-stage builds
- Regular security updates
2. Monitoring Stack
- Prometheus for metrics
- Grafana for visualization
- ELK for log analysis
3. AI Integration
- Model serving infrastructure
- Feature store setup
- Model monitoring tools
