AIOps Architecture Patterns

A well-designed AIOps platform has four layers: Data Ingestion, Intelligence, Automation, and Visualization.

Reference Architecture

┌─────────────────────────────────────────────────┐
│               Visualization Layer               │
│  Grafana │ Custom Dashboards │ Alerting UI      │
├─────────────────────────────────────────────────┤
│               Automation Layer                   │
│  Runbook Automation │ Self-Healing │ ChatOps     │
├─────────────────────────────────────────────────┤
│               Intelligence Layer                 │
│  Anomaly Detection │ Event Correlation │ RCA     │
├─────────────────────────────────────────────────┤
│               Data Ingestion Layer               │
│  Metrics │ Logs │ Traces │ Events │ Topology     │
└─────────────────────────────────────────────────┘

Data Ingestion Layer

Collect data from all sources into a unified data lake:

Data Type	Sources	Tools
Metrics	Infrastructure, applications, custom	Prometheus, Datadog
Logs	Application logs, system logs	Fluentd, Logstash
Traces	Distributed request flows	OpenTelemetry, Jaeger
Events	Deployments, alerts, incidents	PagerDuty, Webhooks
Topology	Service dependencies, infra maps	Service mesh, CMDB

Key Design Decisions

• Use OpenTelemetry as the standard instrumentation framework
• Normalize data at ingestion time — consistent labels, timestamps, formats
• Retain strategically — hot (15 days), warm (90 days), cold (1 year)

Intelligence Layer

This is where ML models analyze operational data:

Anomaly Detection

Detect unusual patterns in metrics and logs:

# Example: Statistical anomaly detection
import numpy as np

def detect_anomaly(values, threshold=3.0):
    """Z-score based anomaly detection."""
    mean = np.mean(values)
    std = np.std(values)
    if std == 0:
        return False
    z_score = abs((values[-1] - mean) / std)
    return z_score > threshold

Approaches:

• Statistical — Z-score, IQR, ARIMA (simple, explainable)
• ML-based — Isolation Forest, LSTM autoencoders (complex patterns)
• Baseline — Compare current vs. historical normal (day-over-day, week-over-week)

Event Correlation

Group related alerts to reduce noise:

1. Time-based — Alerts within a 5-minute window
2. Topology-based — Alerts from related services
3. Pattern-based — Similar alert signatures
4. Causal — Root cause → symptom relationships

Automation Layer

Act on intelligence insights:

• Level 1: Notification enrichment (add context to alerts)
• Level 2: Diagnostic automation (run health checks, gather data)
• Level 3: Remediation automation (restart services, scale resources)
• Level 4: Preventive automation (act before failures occur)

Getting Started

1. Start with data centralization — get all metrics and logs into one platform
2. Build baselines — understand what "normal" looks like for your key services
3. Implement anomaly detection on your top 5 most critical metrics
4. Create automated runbooks for your top 10 recurring incidents
5. Measure MTTR improvement and iterate

Next Steps

• Getting Started with AIOps
• AIOps Strategy Guide (Blog)