4 DevOps projects for beginners to advanced learners

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.

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.

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. My 2 cents : look into the problems these projects are solving, and how AI is changing the way we approach them. Which one are you working on next? • • • If you found this useful.. 🔔 Follow me (Vishakha) for more Cloud & DevOps insights ♻️ Share so others can learn as well

Kubernetes looks simple, until you peek under the hood. I have spoken to hundreds of DevOps engineers, very few were able to explain Kubernetes architecture to me. Breaking architecture down in 8 categories that are worth knowing: 1. Core Objects ↳ Pods, ReplicaSets, Deployments, StatefulSets, Services, Ingress, ConfigMaps, Secrets, PVCs, Jobs, DaemonSets ↳ When to understand ~ To define and manage your applications' fundamental building blocks. 2. Controllers ↳ Deployment Controller, ReplicaSet Controller, StatefulSet Controller, HPA, CronJob Controller, DaemonSet Controller, VPA ↳ When to understand ~ To automate and maintain the desired state of your applications, enabling self-healing and scaling. 3. Architecture Components ↳ API Server, etcd, Scheduler, Controller Manager, Kubelet, Kube Proxy, Cloud Controller Manager ↳ When to understand ~ To grasp the internal workings and brain of a Kubernetes cluster. 4. Runtime ↳ Docker, containerd, CRI-O, Mirantis Container Runtime (MCR) ↳ When to understand ~ To know how containers are actually executed within your cluster. 5. Security & Policy RBAC, Network Policies, Security Contexts, Service Accounts, PSS, Secrets Encryption When to understand ~ To secure your cluster, control access, and enforce best practices for workloads. 6. Observability ↳Metrics Server, Probes (Liveness, Readiness, Startup), Prometheus, Grafana, OpenTelemetry ↳When to understand ~ To monitor the health, performance, and logs of your applications and the cluster. 7. Extensibility ↳ CRDs, Operators, Helm Charts, Kustomize, Admission Controllers ↳ When to understand ~ To customize and extend Kubernetes capabilities for specific application needs. 8. Webhooks ↳ Mutating Webhooks, Validating Webhooks, OPA, Dynamic Admission Controllers ↳ When to understand ~ To intercept and modify/validate Kubernetes API requests for advanced automation and policy enforcement. Did I miss something? ------- Follow Prashant ‎for more AI, DevOps, Cloud insights.

💡 𝐀𝐈 𝐢𝐬 𝐛𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐞𝐯𝐞𝐫𝐲 𝐃𝐞𝐯𝐎𝐩𝐬 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫’𝐬 𝐜𝐨-𝐩𝐢𝐥𝐨𝐭 https://lnkd.in/gZnnxZ8T From debugging Kubernetes to optimizing CI/CD pipelines, the right prompts can save hours of effort. Here are my Top 10 AI Prompts for DevOps Engineers in 2025 🚀 1️⃣ "Analyze my current CI/CD pipeline YAML and suggest optimizations to reduce build time, improve caching, and enhance security." 2️⃣ "I am getting pod crashloop errors in Kubernetes. Here are the logs and kubectl describe pod output. Suggest step-by-step fixes." 3️⃣ "Review this Terraform code and suggest improvements for security, modularity, and cost optimization." 4️⃣ "Generate an incident postmortem report based on these logs, alerts, and timeline of events." 5️⃣ "Analyze AWS cost explorer data and provide recommendations to reduce costs while maintaining HA." 6️⃣ "Here’s my Prometheus/Grafana alerting config. Suggest improvements to reduce alert fatigue." 7️⃣ "Review this Dockerfile and suggest ways to reduce image size and follow container security best practices." 8️⃣ "Here are 500 lines of logs. Identify error patterns, anomalies, and root causes." 9️⃣ "Review this Ansible playbook and recommend changes for idempotency, efficiency, and security." 🔟 "What are the latest DevOps tools and certifications in 2025 that I should learn to stay ahead?" 👉 With the right AI prompts, you can accelerate automation, boost reliability, and focus on innovation over firefighting. If you are someone who can help me with some AI projects hit me on a DM PS: Free DevOps classes starting rom today 6 PM www.heydevops.in / follow Praveen Singampalli for any job help ❤️ Save/Share this post with your community to help :)

If I was completely new to MLOps, this is the repo I’d start with. (from simple concepts to an end-to-end project) It doesn’t assume you’re already an ML expert — it walks you through the basics of ML processes first, then guides you step by step toward building and deploying a complete project. This repo is structured week by week, along with DevOps practices: Week 1 – Introduction & Experiment Tracking (MLflow) Week 2 – Data Versioning & Orchestration (DVC, Prefect) Week 3 – Orchestration continued (Prefect pipelines) Week 4 – Model Deployment (FastAPI, Docker, Kubernetes) Week 5 – Model Monitoring & Observability Week 6 – CI/CD for ML (GitHub Actions, unit/integration tests) Week 7 – Project: end-to-end ML system What I like most about this repo is how it connects the dots — from learning ML concepts → to running pipelines → to deploying and monitoring models in production. GitHub Repo with weekly modules: 🔗 https://lnkd.in/dH2SUPPK If you’ve been waiting to get hands-on with MLOps, you should definitely check this out! • • • I share bite-sized insights on Cloud, DevOps & MLOps (through my newsletter as well) — if this was useful, hit follow (Vishakha) and share it so others can learn too!

I've been thinking for a while about whether I should start writing some DevOps articles, and I finally decided to give it a shot. No AI, just real-world project situations. A few months ago, I faced an interesting challenge: building containers inside a Kubernetes cluster. Initial situation: Pipeline agents were hosted inside Kubernetes clusters, and since version 1.20, the Docker runtime became deprecated. Due to this, I started researching how to build container images using those self-hosted agents. Of course, I came across a few methods: 1. Docker in Docker: Simply run Docker inside a Docker container. The downside is that this requires privileged permissions, which is a big no—containers should not run as root. 2. Kaniko: It runs rootless, so you don’t need root privileges. It can be integrated inside the agent or as a standalone pod. However, it doesn't support TLS certificates or TCP communication. 3. BuildKit: Like Kaniko, it runs rootless and can be integrated into the agent or as a standalone pod. As a bonus, compared to Kaniko, it has a better and faster build cache. It also supports TCP communication and TLS certificates. My personal choice was to go with BuildKit with the following setup: I deployed a rootless BuildKit pod and installed buildctl (the command-line tool for BuildKit) into the agents. Then I created TLS certificates to secure the connection between the BuildKit builder pod and the agents. For the build cache, I opted for remote caching, storing it directly in the container registry. Basically, before building a new image, BuildKit pulls the cache image containing the layers of the previous container image. I chose remote caching because, in my case, I couldn’t use persistent volumes in that Kubernetes cluster. So the setup ended up being a rootless builder, secured and isolated in a private Kubernetes cluster, with the ability to scale the BuildKit pod based on pipeline demand. References: https://lnkd.in/dr5WDpgC https://lnkd.in/dbsWTz9s https://lnkd.in/dj2d2Kjk

Scenario Based DevOps Interview Questions and Answers 131. How do you troubleshoot slow Terraform apply? Answer: Reduce resource count per state → Use -parallelism flag → Split infra into modules → Optimize provider API calls. 132. How do you ensure Docker image immutability? Answer: Tag images with version/commit hash → Push immutable tags to registry → Prevent latest usage in pipelines. 133. How do you troubleshoot Azure Kubernetes Service (AKS) scaling issues? Answer: Check cluster autoscaler logs → Verify VM quotas in Azure → Ensure correct resource requests/limits. 134. How do you handle Jenkins credentials securely? Answer: Store in Jenkins Credentials Manager → Inject at runtime → Rotate periodically → Integrate with Vault/Key Vault. 135. How do you manage stateful applications in Kubernetes? Answer: Use StatefulSets → PersistentVolumeClaims → Ensure proper storage class → Backup with Velero. 136. How do you monitor CI/CD pipeline performance? Answer: Enable Jenkins Prometheus plugin → Visualize in Grafana → Track build duration, failure rate, queue time. 137. How do you troubleshoot GCP Cloud Build quota exceeded error? Answer: Check quotas in GCP console → Optimize build concurrency → Request quota increase → Split builds. 138. How do you enforce policy-as-code in Terraform? Answer: Use Sentinel (Terraform Cloud) or OPA/Conftest → Define rules (naming, tags, security) → Enforce in pipeline. 139. How do you handle Kubernetes secret exposure in logs? Answer: Prevent kubectl describe from showing → Use kubectl get secret -o jsonpath securely → Audit RBAC → Enable encryption at rest. 140. How do you implement CI/CD rollbacks automatically? Answer: Pipeline detects failure → Triggers kubectl rollout undo or redeploys last known good artifact → Notifies team.