4 DevOps projects for beginners to advanced, including AI

If I were starting DevOps from scratch — here are 4 projects I wouldn’t miss (beginner → AI-based advanced) 1. CI/CD Pipeline with Docker & GitHub Actions (Beginner) Set up a pipeline to automatically build, test, and deploy a containerized app. ↳ Tools: Git, Docker, K8s, GitHub Actions ↳ Youtube: https://lnkd.in/drS5DKXz 2. Kubernetes Deployment with Monitoring (Intermediate) Deploy a sample app to Kubernetes and set up Prometheus + Grafana dashboards. ↳ Tools: Kubernetes, Helm, Prometheus, Grafana ↳ Youtube: https://lnkd.in/dsjiwJ_d 3. Infrastructure as Code with Terraform (Advanced) Provision VMs, storage, and networking on a cloud provider using IaC. ↳ Tools: Terraform, Azure ↳ Youtube: https://lnkd.in/ds_Y5xhn 4. AI-Driven Predictive Scaling (Next-Gen) Use KEDA + ML models to autoscale workloads before spikes occur. ↳ Tools: Kubernetes, KEDA, Python (forecasting models) ↳ GitHub: https://lnkd.in/d5C5ie8V With AI taking center stage, DevOps is no longer just about CI/CD pipelines. We’re seeing MLOps for model training pipelines; LLMOps for managing large language models in production; and AIOps for self-healing, predictive cloud operations. Picture Credit: Osereme Okojie

If I were starting DevOps from scratch — here are 4 projects I wouldn’t miss (beginner → AI-based advanced) 1. CI/CD Pipeline with Docker & GitHub Actions (Beginner) Set up a pipeline to automatically build, test, and deploy a containerized app. ↳ Tools: Git, Docker, K8s, GitHub Actions ↳ Youtube: https://lnkd.in/drS5DKXz 2. Kubernetes Deployment with Monitoring (Intermediate) Deploy a sample app to Kubernetes and set up Prometheus + Grafana dashboards. ↳ Tools: Kubernetes, Helm, Prometheus, Grafana ↳ Youtube: https://lnkd.in/dsjiwJ_d 3. Infrastructure as Code with Terraform (Advanced) Provision VMs, storage, and networking on a cloud provider using IaC. ↳ Tools: Terraform, Azure ↳ Youtube: https://lnkd.in/ds_Y5xhn 4. AI-Driven Predictive Scaling (Next-Gen) Use KEDA + ML models to autoscale workloads before spikes occur. ↳ Tools: Kubernetes, KEDA, Python (forecasting models) ↳ GitHub: https://lnkd.in/d5C5ie8V With AI taking center stage, DevOps is no longer just about CI/CD pipelines. We’re seeing MLOps for model training pipelines; LLMOps for managing large language models in production; and AIOps for self-healing, predictive cloud operations. Picture Credit: Osereme Okojie

Enhance your DevOps skills with the projects below 1. CI/CD Pipeline with Docker & GitHub Actions (Beginner) Set up a pipeline to automatically build, test, and deploy a containerized app. ↳ Tools: Git, Docker, K8s, GitHub Actions ↳ Youtube: lnkd.in/drS5DKXz 2. Kubernetes Deployment with Monitoring (Intermediate) Deploy a sample app to Kubernetes and set up Prometheus + Grafana dashboards. ↳ Tools: Kubernetes, Helm, Prometheus, Grafana ↳ Youtube: lnkd.in/dsjiwJ_d 3. Infrastructure as Code with Terraform (Advanced) Provision VMs, storage, and networking on a cloud provider using IaC. ↳ Tools: Terraform, Azure ↳ Youtube: lnkd.in/ds_Y5xhn 4. AI-Driven Predictive Scaling (Next-Gen) Use KEDA + ML models to autoscale workloads before spikes occur. ↳ Tools: Kubernetes, KEDA, Python (forecasting models) ↳ GitHub: lnkd.in/d5C5ie8V With AI taking center stage, DevOps is no longer just about CI/CD pipelines. We’re seeing MLOps for model training pipelines; LLMOps for managing large language models in production; and AIOps for self-healing, predictive cloud operations.

If I were starting DevOps from scratch — here are 4 projects I wouldn’t miss (beginner → AI-based advanced) 1. CI/CD Pipeline with Docker & GitHub Actions (Beginner) Set up a pipeline to automatically build, test, and deploy a containerized app. ↳ Tools: Git, Docker, K8s, GitHub Actions ↳ Youtube: https://lnkd.in/drS5DKXz 2. Kubernetes Deployment with Monitoring (Intermediate) Deploy a sample app to Kubernetes and set up Prometheus + Grafana dashboards. ↳ Tools: Kubernetes, Helm, Prometheus, Grafana ↳ Youtube: https://lnkd.in/dsjiwJ_d 3. Infrastructure as Code with Terraform (Advanced) Provision VMs, storage, and networking on a cloud provider using IaC. ↳ Tools: Terraform, Azure ↳ Youtube: https://lnkd.in/ds_Y5xhn 4. AI-Driven Predictive Scaling (Next-Gen) Use KEDA + ML models to autoscale workloads before spikes occur. ↳ Tools: Kubernetes, KEDA, Python (forecasting models) ↳ GitHub: https://lnkd.in/d5C5ie8V With AI taking center stage, DevOps is no longer just about CI/CD pipelines. We’re seeing MLOps for model training pipelines; LLMOps for managing large language models in production; and AIOps for self-healing, predictive cloud operations.

Applying the skills you've just learned is the best way to master them. If you're learning DevOps here are a few ideas to get started. But don't just copy paste, try coming up with your own twist to the project, it's by making mistakes that'll you grow more not by copy pasting blindly code. Hope you the best. Good luck for your future endeavors.

🤷♂️ "AI is going to change DevOps." We've all heard it. We've all nodded along. But what does that actually mean for your day-to-day job? 🤓 It feels like a future problem. Except... It isn't. The skills that will keep you relevant are being built right now. And if you're not actively learning, you're falling behind. That's why you can't miss this. KodeKloud is running a FREE AI Learning Week, and it's happening as we speak. The catch? It's already started, but it's absolutely not too late to jump in and catch the most critical sessions. This isn't just theory. This is a hands-on sprint to build a real AI Operations Platform from scratch. Here's the breakdown of what's still to come: → Today (Sep 17): Universal Tool Integration ↳ Learn what makes an AI Agent different from a simple tool. ↳ Build "AutoOps" agents that can actually handle incidents for you. (Game-changer) → Tomorrow (Sep 18): Specialized AI Agents ↳ Go deeper. Deploy agents for specific, high-value tasks. ↳ Think: Automated log analysis and compliance checking. → Sep 19: The Capstone Project ✨ ↳ This is where it all comes together. ↳ You'll integrate everything into a unified system and build a self-healing infrastructure. (Yes, you read that right.) Here's the thing — this isn't just about the AI track. When you register for the free week, you get unlimited access to the entire KodeKloud platform. That means: • 130+ expert-led courses • 900+ hands-on labs All for free until September 20th. It's a no-brainer. 🫡 In all seriousness, the pace of AI in our field is intimidating. Opportunities like this are how you stay ahead of the curve. It's one week of focused effort that could fundamentally change your skillset for the next year. Link in the comments below 👇 What's the most exciting part of AI in DevOps for you? Automating incident response? Or building self-healing systems? Let me know in the comments! • • • 🔔 Follow me (Piyush sachdeva) for more real talk on DevOps and cloud! ♻️ Share this so your whole team can level up together.

Top Generative AI + AWS DevOps Projects 🚀 1. Generative AI Log Analyzer & Incident Responder Goal: Automate log analysis and incident response. How it works: Collect logs using Amazon CloudWatch + AWS OpenSearch. Send logs to Amazon Bedrock (LLM) → AI generates summaries & RCA. AI suggests remediation (e.g., “Restart ECS container” or “Scale RDS instance”). Trigger AWS Lambda + Ansible for automated fixes. Send AI-generated incident report to Slack/MS Teams. Innovation: AI acts as a virtual SRE (Site Reliability Engineer). 🚀 2. AI-Generated Terraform & CloudFormation Templates Goal: Speed up IaC (Infrastructure as Code) development. How it works: DevOps engineer prompts Bedrock/CodeWhisperer: “Generate Terraform for a VPC with 2 private subnets, an RDS instance, and an ALB.” AI generates Terraform/CloudFormation code. Code is committed to GitHub → CodePipeline runs tests and deployments. AI provides documentation automatically. Innovation: Reduces IaC development time from hours → minutes. 🚀 3. Generative AI ChatOps Bot for DevOps Teams Goal: Enable natural language deployment & troubleshooting. How it works: Slack/MS Teams bot connected to Amazon Bedrock. Team asks: “Deploy service A to staging.” “Summarize last 2 hours of CloudWatch logs.” “Rollback Lambda function to previous version.” Bot calls AWS CodePipeline / CodeDeploy / Lambda APIs. AI confirms actions and logs them in Confluence/Jira. Innovation: Hands-free, conversational DevOps operations. 🚀 4. AI-Powered CI/CD Test Case Generator Goal: Automate test creation for faster delivery. How it works: Developer pushes code → CodePipeline triggers. Amazon CodeWhisperer (AI pair programmer) generates unit & integration tests. Tests are run via CodeBuild. If anomalies → Bedrock LLM suggests fixes. Innovation: Cuts down manual test-writing effort in DevOps pipelines. 🚀 5. AI-Driven Auto-Scaling & Cost Optimization Goal: Use AI for predictive scaling & cost reduction. How it works: Collect usage metrics via CloudWatch & AWS DevOps Guru. Feed historical data into SageMaker ML models / Bedrock. AI predicts future traffic → recommends auto-scaling policies. Lambda applies changes dynamically. AI generates weekly cost-optimization report for management. Innovation: Combines predictive analytics + automation to save costs.

What’s the first step to DevOps that actually delivers? Forget shiny tools—focus on true business value. I will guide you through. Step 1: Automation everywhere. Get CI/CD running - it is all about speed. Even if all you have is GitHub Actions and Docker, that's enough to get moving. Speed beats fancy in the early days. Step 2: Try containers early. Yes, Kubernetes looks scary at first - but incorporating it pays off fast. It’s not just about scaling, it’s about building things that won’t break in production. And don’t forget monitoring from the start.Monitoring dashboards save you from surprises later. Step 3: Turn your cloud routines into code. Doesn’t matter if you pick AWS, Azure, or GCP—what matters is that your setup isn’t a mystery. Step 4: Prepare for what’s next. The dev-led AI era is here. Think about autoscaling that uses predictive models. If your system knows trouble is coming before it hits, you don’t need to learn the hard way with downtime. Here’s the bottom line: the world is bigger than CI/CD now. AIOps and MLOps are solving headaches we didn’t even have a few years back. But the teams that win? They stick to the basics. Pick projects that move the needle for your business. Ignore every shiny new thing. Level up your skills where it matters for your customers. Simple works. What’s the next DevOps milestone you want to crush?

Scenario Based DevOps Interview Questions and Answers 81. How do you rotate service account keys in GCP/Azure? Answer: Automate with GCP IAM key rotation or Azure Key Vault rotation policies → Update CI/CD pipelines to use new keys. 82. How do you troubleshoot DNS issues in Kubernetes? Answer: Run kubectl exec into pod → Test DNS resolution → Check CoreDNS logs → Restart CoreDNS pods → Fix network policies if blocking. 83. How do you monitor CI/CD pipelines? Answer: Export Jenkins/Azure DevOps metrics → Send to Prometheus/Grafana → Add alerts for failed pipelines and long-running jobs. 84. How do you handle Terraform destroying critical resources accidentally? Answer: Use prevent_destroy in Terraform resource → Enable approval stages in pipelines → Use terraform plan before apply. 85. How do you implement blue-green deployment in Azure DevOps? Answer: Use deployment slots (App Service) → Route traffic between slots → Rollback to blue if green fails. 86. How do you investigate high API latency in GCP/Azure APIs? Answer: Check Cloud Trace / Application Insights → Identify slow endpoints → Scale backend pods → Add caching/CDN. 87. How do you implement GitOps in DevOps workflows? Answer: Use ArgoCD/Flux → Keep infra/app configs in Git → Sync automatically with Kubernetes → Rollback by reverting Git commit. 88. How do you debug slow Docker container startup? • Answer: Check image size → Optimize Dockerfile → Preload dependencies → Monitor entrypoint logs. 89. How do you manage multiple Kubernetes clusters securely? Answer: Use Rancher, Anthos, or Azure Arc → Apply consistent RBAC & policies → Centralized monitoring/logging. 90. How do you handle failed Terraform import of resources? Answer: Verify resource ID → Check provider version → Import manually using terraform import → Update .tf code.

As a DevOps Lead, I’ve realised how MLOps fits into the DevOps world first-hand:  Being effective today isn’t just about code pipelines.   It’s about model pipelines, too.  That’s where AI engineering and MLOps meet DevOps.  I’ve deployed AI models, specialist ML infrastructure, GPU workloads on Kubernetes, and MLOps tooling in the cloud. Here are the fundamentals I’ve learned 👇  ✅ 𝗧𝗿𝗲𝗮𝘁 𝗺𝗼𝗱𝗲𝗹𝘀 𝗹𝗶𝗸𝗲 𝗰𝗼𝗱𝗲   Version them, track changes, and store them with the same discipline as source code.  ✅ 𝗘𝘅𝘁𝗲𝗻𝗱 𝗖𝗜/𝗖𝗗 𝗶𝗻𝘁𝗼 𝗖𝗜/𝗠𝗟   Pipelines should encompass data validation, model training, and drift detection, not just build and deploy models.  ✅ 𝗗𝗲𝘀𝗶𝗴𝗻 𝗳𝗼𝗿 𝗿𝗲𝗽𝗿𝗼𝗱𝘂𝗰𝗶𝗯𝗶𝗹𝗶𝘁𝘆   Whether you’re running GPU-heavy training on Kubernetes or serving models through inference endpoints, you should be able to reproduce results on demand. Containers, IaC, and automation make this possible.  ✅ 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝗯𝗲𝘆𝗼𝗻𝗱 𝘂𝗽𝘁𝗶𝗺𝗲   Applications fail fast, models fail slow. Metrics like drift, bias, and accuracy matter just as much as CPU, memory, or GPU utilisation.  ✅ 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝘁𝗼𝗼𝗹𝗶𝗻𝗴   Kubeflow, MLflow, Ray and Cloud Provider AI Solutions are powerful, but they need strong DevOps principles (IaC, CI/CD, CI/ML, observability) to deliver value.  ✅ 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗲 𝗮𝗰𝗿𝗼𝘀𝘀 𝘁𝗲𝗮𝗺𝘀   DevOps, data engineering and data science often speak different languages. The real wins come when infrastructure, tooling, and models are aligned around shared ownership.  Becoming an AI & MLOps engineer in a DevOps world isn’t about chasing buzzwords. It’s about combining DevOps discipline with specialised AI/ML infrastructure so that models don’t just get built, but also get delivered, scaled, and trusted.  👉 Where have you seen the biggest challenges in running AI/ML workloads in production?