AI AIOps Engineering: Deploy AI Models at Scale

Step 1 – Solid DevOps & Observability Foundation

• Make sure you are comfortable with Linux, Git, Docker, Kubernetes and CI/CD (Jenkins / GitHub Actions).
• Learn observability basics: metrics, logs and traces using tools like Prometheus, Grafana and OpenTelemetry.
• Define SLOs / SLIs and understand what “normal” looks like for your services before introducing AI.

Step 2 – Python & Data Skills for AIOps

• Learn Python basics: functions, modules, virtual environments and working with JSON / CSV.
• Use libraries like pandas and NumPy to clean and analyse log and metric data exported from your monitoring tools.
• Build small scripts that detect anomalies in error counts or latency and send notifications to Slack or email.

Step 3 – Machine Learning for Incidents and Anomalies

• Understand core ML ideas used in AIOps: classification, clustering and anomaly detection.
• Train simple models that detect unusual spikes in CPU, memory or error rates using time‑series techniques.
• Integrate ML predictions into your alerting flow – for example, only page on‑call when the model thinks a pattern is truly abnormal.

Step 4 – LLMs and Log Intelligence

• Use LLM APIs to summarise long log files, failed pipeline runs or Kubernetes events into human‑readable root‑cause hints.
• Build a small “Chat with Logs” tool that takes context from your logs / metrics store and passes it to an LLM for natural language queries.
• Generate draft incident reports and post‑mortems using AI, then review and refine manually.

Step 5 – Deploying AI Models on Kubernetes

• Package inference code in Docker images and deploy to Kubernetes as microservices (for example, a log‑anomaly API).
• Use autoscaling (HPA) so your AI services scale up when traffic or data increases.
• Secure AI endpoints with proper authentication and resource limits to avoid noisy‑neighbour problems on the cluster.

Step 6 – AIOps Use‑Cases to Add to Your Resume

• Intelligent alerting: reduce noise by grouping and enriching alerts before sending them to on‑call engineers.
• Auto‑remediation: run scripts or workflows automatically when certain patterns or incidents are detected.
• Capacity and cost optimisation recommendations based on usage patterns across your AWS accounts and clusters.

Next Step – Move into AI‑Powered DevOps Roles

To implement these ideas with real projects, you can combine our DevOps Engineering course with an AI Cloud DevOps / AIOps program, where you will build log‑analysis bots, AI‑powered dashboards and auto‑remediation pipelines.

For counselling or a personalised AIOps learning plan, contact the We Tech Zone team via the contact page or WhatsApp number shown on the site.