Ali Amari

AIOps vs LLMOps vs MLOps vs DevOps — what’s the difference? and how AI is creating new opportunities for DevOps Engineers Here devops is the central process — the rest are extensions tailored to different workloads. Each has its own distinct focus 👇 DevOps → evolving with AI → No longer just about CI/CD and containerization — it’s the backbone of modern workflows. → AI helps auto-generate configs, optimize pipelines, and detect deployment risks. 📌DevOps Role: set up CI/CD, manage infra as code, and leverage AI copilots to ship features faster and safer. ⸻ MLOps → models in production → No longer just training models — it’s about deploying and keeping them reliable at scale. → Focuses on data versioning, pipeline orchestration, and drift monitoring. 📌DevOps Role: build Kubernetes pipelines for ML, integrate monitoring for models, and ensure reproducibility with tools like MLflow/DVC. ⸻ AIOps → intelligence in operations → Detect anomalies and incidents with AI/ML. → Enable predictive scaling and self-healing systems. 📌 DevOps Role: connect monitoring systems to AIOps platforms, fine-tune detection thresholds, and test auto-remediation workflows. ⸻ LLMOps → serving large language models → Fine-tune, serve, and scale LLMs in production. → Manage inference costs, latency, and guardrails for safe outputs. 📌 DevOps Role: deploy LLM inference on GPUs, optimize autoscaling, and set up guardrails and monitoring for cost and safety ⸻ So no — AI won’t replace DevOps jobs. In fact, the field is expanding for those ready to adapt. • • • If you found this useful.. 🔔 Follow me (Vishakha) for more Cloud & DevOps insights ♻️ Share so others can learn as well!

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⎈ 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐈𝐧𝐠𝐫𝐞𝐬𝐬 𝐌𝐚𝐬𝐭𝐞𝐫𝐲 – Complete Guide for DevOps Engineers ╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍ From Zero ➝ Production, everything a DevOps engineer needs to know: ✅ Real-world examples ✅ CI/CD Integration ✅ Controller Comparison ✅ Best Practices A clear and structured guide to simplify service exposure and master Ingress in production. ╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍ ♻️ If this inspires you, share it with your network! 🔴 Follow me for more #Kubernetes & #DevOps 👉 https://lnkd.in/deugUkHT ╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍ #Kubernetes #DevOps #CloudNative #CKA #CKAD #CKS #CI/CD

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Why Many DevOps CVs Fail to Stand Out — and How to Fix Yours After years of seeing how DevOps roles are advertised, discussed, and filled, one thing is clear: too many CVs sound exactly the same. The Usual “Tool List” Approach: I. Docker & Kubernetes experience II. CI/CD pipeline implementation III. Infrastructure as Code (IaC) IV. Monitoring & observability V. Cloud platforms (AWS, Azure, GCP) In 2025, listing these without context is like writing “Proficient in Microsoft Word.” What Gets Noticed Instead: I. Specific results: “Reduced deployment time from 4 hours to 8 minutes.” II. Real problems solved: “Eliminated 3 AM alerts by adding automated health checks.” III. Business impact: “Improved system uptime from 99.9% to 99.99%, preventing £50k in lost sales.” IV. Learning from challenges: “Introduced chaos engineering after a major outage.” V. Sharing knowledge: “Built an internal DevOps onboarding program for new hires.” The “Show, Don’t Tell” Principle: Add a link to your GitHub with actual infrastructure code. Publish a short write-up about a technical challenge you solved. Highlight open-source contributions. Share talks or presentations you’ve delivered. Example of a Stand-Out CV Line: "Built a deployment pipeline allowing a small team to ship 40+ releases/week without weekend work." That shows automation skills, a focus on efficiency, and the ability to scale both systems and teams. Takeaway: Don’t just list the tools you’ve touched. Tell the story of your impact. That’s what employers and collaborators remember. — Chigozie Ozoemena #StackedByChigozie AWSWithChigozie

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🔖#Linux #CommandLine #DevOps #SysAdmin #CloudComputing #SRE 🚀 Master the Linux Command Line – The Superpower You Didn’t Know You Needed! In today’s fast-paced tech world, Linux is everywhere – powering servers, cloud platforms, cybersecurity labs, IoT devices, and even your favorite websites. But here’s the truth: the real magic of Linux lies in mastering the command line. While GUIs are convenient, the terminal is where speed meets control. Imagine automating repetitive tasks in seconds, managing servers across continents, or troubleshooting complex issues without breaking a sweat – all possible when you speak Linux’s native language. From grep for searching like a pro, awk for data manipulation, to chmod for security – each command unlocks a layer of mastery. And the best part? The more commands you know, the more they combine into powerful workflows. Whether you’re a developer, system admin, ethical hacker, or data engineer, Linux commands are your universal passport in tech. 💡 Pro tip: Don’t just memorize commands – understand why they work. That’s how you turn from a casual user into a Linux power player.

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🚀 Master #Kubernetes — One Question at a Time! Whether you’re preparing for interviews, aiming to sharpen your container orchestration skills, or just starting your Kubernetes journey — I’ve compiled 100 carefully curated Kubernetes questions that cover: ✅ Core concepts & architecture ✅ Pods, Deployments, Services ✅ ConfigMaps & Secrets ✅ Networking & Storage ✅ Security & RBAC ✅ Troubleshooting tips 📄 Free PDF Download: Kubernetes 100 Questions Perfect for DevOps engineers, cloud architects, and anyone looking to level up their K8s skills. 💡 The best way to learn? Test yourself regularly and fill the gaps as you go! For more such insights, follow Manjunath Kotegar #Kubernetes #CloudComputing #DevOps #Containers #CKA #CKAD #CloudNative #Docker #Orchestration #LearningResources

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