Raees Qazi (RYQ's) | DevOps Engineer | Learner | Mentor | Creator

 Your 2026–2030 Roadmap, Salary Trends, Career Advice, FAQs & Final Thoughts In Part 1, we explored why AI has created fear across the DevOps community and why so many professionals are questioning the future of their careers. In Part 2, we separated fact from fiction by identifying the tasks AI can automate and the responsibilities that still require human expertise. Now comes the most important question: How do you prepare for the next five years? Technology has always rewarded engineers who adapt early. Cloud computing, containers, Kubernetes, Infrastructure as Code, and CI/CD all created new opportunities for professionals willing to learn. Artificial Intelligence is no different. The future belongs to engineers who combine technical expertise with AI — not to those who ignore it. Let’s build a roadmap that keeps your career relevant through 2030. The Future DevOps Engineer The DevOps engineer of 2030 will look very different from the DevOps engineer of 2020. Five years ago...

 What AI Can Replace, What It Can’t, and the Future of DevOps In Part 1, we explored why Artificial Intelligence has created fear across the DevOps community and why so many engineers are questioning the future of their careers. Now it’s time to answer the question everyone is asking: What can AI actually replace? The answer might surprise you. The truth is that AI is incredibly good at certain tasks — but surprisingly limited in others. Understanding this difference is the key to building a future-proof DevOps career. What AI Can Already Replace Let’s be realistic. Artificial Intelligence has become extremely capable over the last few years. If you’ve used ChatGPT, GitHub Copilot, Claude, or Gemini, you’ve probably seen how quickly they generate code and solve technical problems. In many cases, AI can complete tasks in seconds that previously took engineers hours. Here are the areas where AI is already making a huge impact. 1. Writing Infrastructure as Code (IaC) Need a Terraform ...

  Is AI Really Coming for DevOps Jobs? “Will AI replace DevOps engineers?” If you’ve searched this question recently, you’re not alone. Over the past two years, Artificial Intelligence has transformed the way software is built, tested, deployed, and managed. Tools like ChatGPT, GitHub Copilot, Claude, Gemini, and AI-powered coding assistants have become everyday companions for developers and DevOps professionals. Tasks that once took hours can now be completed in minutes. Naturally, this has created one of the biggest fears in the tech industry: “If AI can write scripts, generate Terraform code, create Kubernetes manifests, and troubleshoot errors, do companies still need DevOps engineers?” It’s a valid question. Every week, thousands of professionals search for answers because nobody wants to spend years mastering DevOps only to discover that AI has taken over the job market. But here’s the reality: AI is changing DevOps forever — but it is not eliminating the profession. Instead,...

From an Idea to a Live AI Assistant Artificial Intelligence is transforming software development faster than ever before. A few months ago, building a conversational AI assistant required a team of machine learning engineers, expensive infrastructure, and months of development. Today, thanks to modern Large Language Models (LLMs), open-source frameworks, and cloud computing, a single developer can build and deploy a production-ready AI chatbot. In this blog, I’ll share how I built my own AI chatbot locally and successfully deployed it on AWS EC2. I’ll explain the complete architecture, technologies used, deployment strategy, and lessons learned throughout the project. Project Overview The goal was simple: Build an AI chatbot that can communicate naturally with users while running on my own infrastructure instead of relying on third-party chatbot platforms. The chatbot should be able to: Answer user questions naturally Maintain conversational flow Respond quickly Be deployable on AWS Be...

I’m happy to share that I have successfully completed my DevOps learning journey . Alongside learning, I also built and shared educational content through blogs and videos on different platforms like Medium, Blogspot, YouTube, LinkedIn, Facebook, and TikTok. 🔗 My Profiles & Content Links LinkedIn: https://www.linkedin.com/in/raees-yaqoob-qazi-ryqs/ Medium: https://medium.com/@raeesyaqubqazi Blogspot: https://brillertechnologies.blogspot.com/ Facebook: https://web.facebook.com/profile.php?id=61553548371216 YouTube: https://www.youtube.com/@RaeesQ TikTok: https://www.tiktok.com/@mrryqs?_t=ZS-8y7t0fQfJKu&_r=1 🧠 What I Covered in My DevOps Journey During this journey, I worked on and learned the core DevOps stack: 🐧 Linux (System basics & administration) 🔧 Git & GitLab (Version control & collaboration) 🐳 Docker (Containerization) ☁️ AWS (Cloud fundamentals & services) ⚙️ Jenkins (CI/CD pipelines) ☸️ Kubernetes (Container orchestration) 📦 Terraform (Infrastruc...

 Today, I’m going to discuss a very important concept in Python: How to raise an Exception in code and why we use it. As DevOps engineers, we often write automation scripts, CI/CD pipelines, and cloud management tools. In real-world projects, sometimes we want our program to stop execution when something unexpected happens. This is where raise Exception becomes useful. What is raise Exception ? The raise keyword is used to manually generate an exception. When Python encounters a raised exception that is not handled, it immediately stops the program. Example 1: Raising an Exception Outside a Try Block cloud_envs = [ "aws" , "gcp" , "azure" ] try: print(cloud_envs[4]) except: print("Exception handled") finally: print("I will execute anyways") print("This code should run") raise Exception("This is a new Exception") Output Exception handled I will execute anyways This code should run Exception: This is a...