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- Week 7: Observability & AIOps
Week 7: Observability & AIOps
The final piece of the Cloud DevOps puzzle
Vishakha Sadhwani
October 10, 2025
In partnership with
Hi there, Inner Circle,
Welcome to the final week of becoming Cloud DevOps Ready!
I had some big announcements to make this week.. so thank you for your patience with this edition.
Let’s dive right in.
This Week’s Focus → Day 2 Operations
We’re exploring the Observability Stack — understanding the main tools, key concepts, and a few advanced topics like Service Mesh and AIOps.
The Observability Stack: Core Components
Metrics — The numbers behind the system
Quantitative data that shows how your systems perform: CPU utilization, memory usage, request latency, error rates, and throughput.
Tools:
→ Prometheus – open-source monitoring and alerting toolkit widely used with Kubernetes.
→ Cloud Monitoring – managed service on GCP for collecting metrics, dashboards, and alerts.
→ Datadog – full-stack observability platform used across enterprises for monitoring hybrid workloads.
(Different organizations use different tools depending on their ecosystem and scale.)
Resources:
Logs — The most used component during troubleshooting
Everything your app does is logged for auditing and debugging. Logs record events, errors, and system activities across the stack.
If you’re retrieving logs directly from servers, here are common directories by OS:
OS | Where to Find System Logs |
|---|---|
Linux (Debian/Ubuntu) | /var/log/syslog,/var/log/auth.log,/var/log/dmesg,/var/log/kern.log |
Linux (Red Hat/CentOS) | /var/log/messages,/var/log/secure,/var/log/dmesg |
Windows Server | View in Event Viewer (eventvwr.msc) or as.evtxfiles inC:\Windows\System32\winevt\Logs\ |
Tools:
→ Cloud Logging – managed logging service on GCP that integrates with Monitoring and IAM.
→ Fluentd – open-source log collector that unifies data collection across sources.
→ Loki – log aggregation system by Grafana Labs optimized for containerized environments.
→ ELK Stack (Elasticsearch, Logstash, Kibana) – open-source stack for large-scale log analysis.
(Each tool fits a different scale and infrastructure type.)
Resources:
You can further check the tool specific documentation.
Tracing — The Path of a Request
Tracks requests moving across multiple services. And if there is any failure in any of the servoces
Tools:
→ OpenTelemetry – open standard for collecting and exporting telemetry data (metrics, logs, traces).
→ Jaeger – distributed tracing platform often used with Kubernetes-based microservices.
→ Zipkin – simple distributed tracing system to identify latency issues.
→ Cloud Trace – GCP’s native distributed tracing service.
Resources:
Alerting — The System That Wakes You Up at 2 A.M.
Alerts when metrics/logs cross a threshold.
Tools:
→ Cloud Monitoring Alerts – Cloud native alerting and notification system.
→ PagerDuty – industry-standard incident management and escalation platform.
→ Opsgenie – Atlassian’s on-call and alert orchestration tool.
→ Prometheus Alertmanager – handles alerts from Prometheus and routes them via email, Slack, or webhooks.
Resources:
Advanced Observability
Service Mesh (Istio / Linkerd / AWS App Mesh)
A dedicated network layer for microservices that manages traffic, observability, and security without changing application code.
Key Benefits:
→ Uniform observability across all services
→ Built-in metrics and tracing
→ Traffic management (blue/green, canary, retries)
AIOps — Intelligence Meets Operations
AIOps applies AI and ML to analyze operational data at scale, automate detection, and predict incidents before they occur… basically if you can use a model to summarize incident reports, improve ops tasks like anomaly detection, recommending fixes etc.
Real-world examples:
→ Detecting anomalies in metrics before downtime hits
→ Automated incident triage and root cause prediction
→ Smart noise reduction in alerting
Tools → Moogsoft, Dynatrace, Splunk ITSI, Datadog AI
Resources:
MlOps vs AiOps (common misconceptions)
🧩 Projects to Wrap Up This Series
Project 1: Proemethues observability Stack
Deploy an end-to-end monitoring and alerting setup using Prometheus, Grafana, Loki, and Alertmanager.
This project helps you visualize system health, collect container metrics, and set up proactive alerting.
GitHub Repository: Prometheus Observability Stack
Project 2: AIOps for Log Analysis
Build an AI-assisted DevOps workflow that uses ML models to analyze logs, detect anomalies, and predict potential incidents.
You’ll explore how AIOps augments traditional DevOps observability pipelines.
GitHub Repository: AIOps for Log Analysis
Wrapping Up
And with that, you’ve completed the “Cloud DevOps Readiness” series! 🎉
From Infrastructure → CI/CD → Security → Observability — now you have the full 360° view.
Drop your feedback, learnings, or questions in the comments — I’ll be responding personally this week.
Next week, I’ll be announcing what’s coming next ~ more advanced, hands-on journeys!
See you soon,
— Vishakha
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