#Devops With Ai

400% hike with these DevOps projects🚀 Comment "Projects" for the guide (DevOps, DevOps Projects, DevOps and Agentic Ai, Agentic AI, Cloud projects, Job, Hiring, Job switch, Software engineering, Trending) #devops #softwareengineering #agenticai #explorepage #trending

ULTIMATE DevOps Project 🔥 In this project, you’ll move through the same stages many real DevOps pipelines follow: containerizing an application with Docker, pushing images to a registry, setting up CI pipelines, adding security scans, deploying to Kubernetes, implementing GitOps with ArgoCD, and finally monitoring everything with Prometheus and Grafana. When you connect these pieces, you stop thinking in isolated tools and start understanding how modern cloud systems are actually operated. Comment “PROJECT” and I’ll send you the full guide to build it in your DMs. . . [devops projects, kubernetes project, devsecops tetris project, docker tutorial, kubernetes tutorial, ci cd pipelines, gitops workflow, prometheus, grafana, devops roadmap, interview prep, cloud engineer, devops engineer, ai engineer, data engineer, software engineer, resume projects, projects for resume, full stack development projects]

Comment “DevOps” #code#ai#devops#engineer#office

"New to DevOps?" Everything feels like secret alien tech! 🤯 But don’t worry, every expert was once confused too. Just keep learning. 💻😂 #DevOps #tools #NewbieVibes #TechHumor #meme #joindevops #hyderabad #bangalore

Resources to learn DevOps 🔥 1. DevOps Roadmap 🧙‍♂️ YT : Hitesh Choudhary 2. DevOps as a career ⚠️ YT : Techworld with Nana 3. DevOps Roadmap 👀 YT : Kunal Kushwaha All the resources are free 🆓 on Youtube ❄️ #devops #aws #cloudcomputing #linux #python #cloud #technology #programming #developer #coding #kubernetes #devopsengineer #azure #cybersecurity #software #java #it #datascience #tech #docker #javascript #softwaredeveloper #css #machinelearning #iot #microsoft #softwareengineer #ai #devopstools #jenkins

Learn devops , cloud , AI for free …. Comment “free” to get this guide…. #devops #cloud #devopsengineer #ai #reels

Learn DevOps in 10 Steps 🔥 | Complete DevOps Roadmap for 2025 - 2026 | Step-by-Step Guide for Beginners Are you ready to become a **DevOps Engineer** in 2025? In this video, I’ll walk you through the **complete DevOps roadmap** — from Linux to Cloud and Python automation. Whether you’re just starting your IT career or want to level up your skills, this guide covers everything you need to master DevOps tools and practices. ### 🧩 Topics Covered: 1️⃣ Linux – Commands, file system & networking basics 2️⃣ Shell Scripting – Automate repetitive tasks 3️⃣ Git & GitHub – Version control and collaboration 4️⃣ CI/CD – Jenkins, GitHub Actions, GitLab CI pipelines 5️⃣ Docker – Containerization fundamentals 6️⃣ Kubernetes – Orchestrate & scale containers 7️⃣ Ansible – Configuration management 8️⃣ Terraform (IaC) – Infrastructure as Code 9️⃣ Cloud (AWS, Azure, GCP) – Real-world deployment 🔟 Python – Automation, scripting & custom tools ### 💡 Perfect For: * Beginners starting a career in **DevOps** * Software engineers & system admins * Anyone who wants to understand modern **Cloud & DevOps practices** --- ### 📚 Keywords: DevOps roadmap 2025, learn DevOps step by step, DevOps tutorial for beginners, DevOps tools explained, CI/CD pipeline, Docker and Kubernetes, Terraform and Ansible, AWS DevOps, how to become a DevOps engineer, cloud computing 2025 --- ### 🔖 Hashtags: #DevOps #DevOpsRoadmap #DevOpsEngineer #LearnDevOps #CloudComputing #AWS #Azure #GCP #Docker #Kubernetes #Ansible #Terraform #Python #CICD #GitHub #Linux

Day-1 of Learning DevOps 🧑🏻‍💻 #devops #devopsengineer #ai #softwaredevelopers #promptengineering #automationengineering #chatgptprompts #chatgpt4

Day 056 | Will AI replace Devops? Everyone’s asking… “Is DevOps dying?” Wrong question. The real question is: How are YOU evolving with it? DevOps isn’t disappearing. It’s evolving to meet modern engineering demands. 🤖 What AI WILL Do: • Automate tasks — not jobs • CI/CD pipeline automation • Log analysis & monitoring • Cost optimization & autoscaling • Repetitive scripting AI handles the busy work so engineers can focus on impact. 🚀 What AI CAN’T Replace: • Human skills & leadership • System architecture decisions • Business risk & production judgment • Strategic thinking & collaboration • Complex troubleshooting DevOps needs context, accountability & intuition. AI doesn’t have that. 🔥 The Future = Human + AI DevOps engineers are becoming: • Automation overseers • Cloud & platform architects • Security & reliability leaders AI won’t replace DevOps engineers. DevOps engineers who use AI will replace those who don’t. — I’m planning something big 👀 Should I create a full AI for DevOps course on my YouTube channel? Comment “YES” if you’d watch it. Comment “CURRICULUM” if you want to see the syllabus first. #DevOps #AI #CloudEngineering #TechCareers #Automation SRE EduwiseSolutions

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DevOps vs. MLOps vs. LLMOps, explained visually 🔧 Many teams are trying to apply DevOps practices to LLM apps. But DevOps, MLOps, and LLMOps solve fundamentally different problems. DevOps is software-centric: Write code, test it, deploy it. The feedback loop is straightforward: Does the code work or not? MLOps is model-centric: You're dealing with data drift, model decay, and continuous retraining. The code might be fine, but the model's performance can degrade over time because the world changes. LLMOps is foundation-model-centric: You're typically not training models from scratch. Instead, you're selecting foundation models and optimizing through three paths: → Prompt Engineering → Context/RAG Setup → Fine-Tuning But here's what really separates LLMOps: The monitoring is completely different. MLOps monitoring: ✅ Data drift ✅ Model decay ✅ Accuracy LLMOps monitoring: ✅ Hallucination detection ✅ Bias and toxicity ✅ Token usage and cost ✅ Human feedback loops This is because you can't just check if the output is "correct." You need to ensure it's safe, grounded, and cost-effective. The evaluation loop in LLMOps feeds back into all three optimization paths simultaneously. Failed evals might mean you need better prompts, richer context, OR fine-tuning. So it's not a linear pipeline anymore. One more thing: prompt versioning and RAG pipelines are now first-class citizens in LLMOps, just like data versioning became essential in MLOps. The ops layer you choose should match the system you're building. 👉 Over to you: What does your LLM monitoring stack look like? #ai #devops #mlops

Comment "DEVOPS" for the links. You Will Never Struggle With DevOps Again 📌 Learn DevOps and Cloud the practical way: 1️⃣ DevOps From Zero to Hero — build and deploy a real production API step by step 2️⃣ AWS Tutorial for Beginners — understand the core cloud services every engineer uses in 2025 3️⃣ Docker & Kubernetes Fundamentals — learn containers and orchestration with hands-on examples Stop getting lost in buzzwords. These resources walk you through the exact skills companies expect: deploying real apps, working with cloud infrastructure, containerizing services, and understanding the DevOps workflow end to end. Whether you're preparing for your first DevOps job, trying to understand cloud systems, or building production-ready projects, these will help you level up way faster. Save this, share it, and start building real DevOps skills with confidence.