20 DevOps + AI Projects to Build in 2026

Hi Inner Circle,

Here’s a list of DevOps + AI projects that you should definitely explore if you want to move beyond just learning tools and actually start understanding systems.

This collection ranges from:
→ beginner-friendly infrastructure projects
→ Kubernetes and GitOps setups
→ observability and AIOps workflows
→ AI agents for infrastructure
→ production-grade GenAI systems

Check it out ~

Beginner Level

If you're new, these five teach you the muscle memory: scripting, containers, pipelines, and your first Kubernetes deploy.

1. Python Script to Communicate with GitHub APIs 

→This is where I'd start ~ it's the smallest possible "real" automation project.

→ You'll learn how APIs, tokens, and JSON responses actually work, which every other DevOps tool builds on. Tools: Python, GitHub APIs

2. CI/CD Pipeline with Docker & GitHub Actions 

→ Your first real pipeline. By the end you'll understand what "build, test, deploy on every push" actually means, and GitHub Actions is the friendliest way to learn it. Tools: Git, Docker, GitHub Actions

3. Deploy 2048 Game on Kubernetes with ALB Ingress 

→ This is the project that makes Kubernetes click. You deploy something fun, expose it to the internet, and finally see how pods, services, and ingress fit together. Tools: Kubernetes, ALB Ingress Controller

4. Reduce Docker Container Size using Multi-stage Builds 

→ A quick win that teaches a habit you'll use forever. Watch your image shrink 10x and you'll never write a single-stage Dockerfile again. Tools: Docker

5. Infrastructure as Code with Terraform 

→ The moment you stop clicking around in cloud consoles. Terraform with Azure here, but the concepts ~ state, providers, modules.. apply to any cloud. Tools: Terraform, Azure

Intermediate Level (Now Make It Production-ish)

You can deploy. Now learn how production teams actually run things ~ monitoring, GitOps, and cost control.

1. Kubernetes Deployment with Monitoring 

→ Deploying is easy; knowing your app is healthy is the real skill. Prometheus + Grafana is the industry-standard stack, and this is the cleanest way to learn it. Tools: Kubernetes, Helm, Prometheus, Grafana

2. Automated Jira Ticket Creation using Python Flask 

→ A taste of glue code, which is honestly 50% of the job. You'll see how a small Flask service can bridge tools your team already uses. Tools: Python, Flask, Jira APIs

3. Terraform Configuration for VPC + App Deployment 

→ Networking is where most cloud projects quietly break. Building a real AWS VPC in code is the single best way to actually understand subnets, routes, and security groups. Tools: Terraform, AWS Networking

4. Online Boutique Microservices Deployment with GitOps 

→ Your first taste of "Git is the source of truth." ArgoCD watches your repo and syncs your cluster to match — once you see it work, you won't want to deploy any other way. Tools: Kubernetes, ArgoCD, Docker, Helm, GitOps

5. Cloud Cost Optimization on AWS using Lambda 

→ The project that pays for itself. You'll write a serverless function that hunts down unused resources — a skill every team genuinely needs. Tools: AWS Lambda, Serverless

6. Dockerfile Generation using Local LLMs 

→ A fun bridge into the AI side of DevOps. You'll wire up a local LLM to write Dockerfiles for you — no API keys, no cost, just hands-on with how LLMs slot into developer workflows. Tools: Local LLMs, Docker

Advanced Level ~ AI-Powered Workflows

This is the modern DevOps stack: AI for anomalies, agents for ops, RAG for runbooks. Do these to stay relevant.

1. AIOps for Log Analysis using Isolation Forest 

→ Your first ML-in-ops project, and a great way to stop writing regex for log parsing. You'll see how unsupervised models find anomalies humans miss. Tools: Python, Isolation Forest, Log Analysis

2. AI-Driven Predictive Scaling using KEDA 

→ HPA reacts to load ~ KEDA + a model predicts it. This is the next-gen autoscaling pattern more teams are adopting in 2026. Tools: Kubernetes, KEDA, Python

3. Create your First AI Agent for Kubernetes using CrewAI 

→ Your hands-on intro to agentic ops. Build agents that diagnose cluster issues and suggest fixes ~ this is where the field is actually heading. Tools: Kubernetes, CrewAI, Python

4. Enforce Advanced Kubernetes Security using Kyverno 

→ Policy-as-code without the OPA/Rego learning curve. You'll block insecure pods and enforce image rules at admission time — security folks will love you. Tools: Kubernetes Security, Kyverno

5. Migrate Infrastructure from CloudFormation to Terraform 

→ A migration every AWS team eventually faces. Doing it once on a small scale is the best prep for doing it at work on a big one. Tools: Terraform, CloudFormation

6. CI/CD using Jenkins, Docker, Kubernetes & ArgoCD 

→ The enterprise stack you'll meet at most large companies. Jenkins builds, ArgoCD deploys — learn this split and you'll fit into any mature pipeline. Tools: Jenkins, Docker, Kubernetes, ArgoCD

7. Create In-house AI Agent for Internal Documentation 

→ A RAG project with real-world payoff. Build a bot that knows your runbooks so you stop pinging the senior engineer at 2 AM. Tools: LLMs, RAG, Vector Databases

8. Complete End-to-End GenAI Project on AWS 

→ A full GenAI app on serverless AWS — Bedrock + Lambda + Python, no GPU management. The cleanest way to ship your first production-ish AI feature. Tools: AWS Bedrock, AWS Lambda, Python

9. AIOps Assistant for Intelligent Incident Analysis 

→ The capstone that ties everything together — logs, metrics, anomalies, and an LLM stitching them into actual incident insight. If you build only one advanced project, build this. Tools: Python, LLMs, AIOps, Prometheus, Grafana

Try at least 1 project out this week.

Good luck, and have fun building :)