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

  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...