Presented AI in DevOps at McDonald's, discussed efficiency, consistency, and innovation.

🚀 AI in DevOps: Smarter CI/CD Pipelines 🤖⚙️∞ The future of DevOps isn’t just automation — it’s intelligence. AI agents are helping teams: ✅ Predict test failures ✅ Automate deployments safely ✅ Reduce alert fatigue ✅ Deliver faster with confidence Humans + AI working together = stronger, resilient pipelines. This is how we move from automated → autonomous DevOps. 🔗 Read the full blog here: 👉 AI Agents in DevOps: Automating CI/CD Pipelines https://lnkd.in/d25ANmiR 💡 Would you trust an AI agent to deploy your code on a Friday evening? #AI #DevOps #Automation #CICD #AIagents #FutureOfWork #AIOps

I’ve decided to dive into the world of AI & ML-assisted DevOps. As DevOps continues to evolve, I want to explore how AI and ML can make systems more intelligent, automated, and resilient—from smarter monitoring to predictive scaling and beyond. Here’s my learning roadmap to start with: 🔹 Understand how AI/ML fits into DevOps workflows 🔹 Explore tools for intelligent monitoring & alerting 🔹 Learn predictive scaling & anomaly detection techniques 🔹 Build small projects combining AI with DevOps automation This is a step to upskill myself and understand the future of DevOps better. I’ll be sharing my learnings along the way, and I’d love to connect with others who are also exploring this exciting space. #DevOps #AI #MachineLearning #AIDevOps

Last week, I read about #Ciroos - a startup building AI #SRE teammates that autonomously manage incidents and streamline operations. That got me thinking: we’re no longer in “AI-augmented” DevOps. We’re in AI-autonomous DevOps. Here’s what that future looks like (some of it is already here) • AI agents scan logs, detect anomalies, and surface root causes before humans even notice. • During high-load events (like Black Friday or product launches), AI predicts traffic spikes and provisions infrastructure ahead of time. • AI breaks down a goal (“optimize system latency”) into subtasks, then carries them through multiple steps automatically - configuration, monitoring, feedback loop - with minimal human intervention. In short: DevOps is shifting from “you push changes” to “you orchestrate intelligence.” But here’s the real danger for engineers and organizations: If you’re still optimizing pipelines, you’re already behind. If your DevOps is not agent-ready, it will get eaten. So ask yourself: Are you building models or tooling that can own parts of your stack? Or are you just stacking wrapper layers around manual processes? 🔥 The future of DevOps won’t be “Dev + Ops.” It’ll be DevOps + Autonomous Intelligence. #AI #DevOps #ML #Sudheerwrites #AIOps #AgenticAI #FutureOfWork #Innovation

⚡ AI + DevOps: Small Models, Big Impact The hype cycle is full of “AI-powered DevOps platforms,” but most of what’s out there are point solutions—useful in pieces, but not full lifecycle coverage. This is where mixture-of-experts (MoE) models, like DeepSeekMoE, could shine. Instead of one bloated model doing everything poorly, MoE allows specialized “experts” to handle tasks like: 🔹 Accelerating flaky test detection 🔹 Translating natural language into policy code 🔹 Offering context-aware pipeline remediation DevOps is complex and fragmented by nature. MoE models fit that world better than one-size-fits-all AI. The future of AI in DevOps may not be a single giant model—it might be specialists working together. 👉 How do you see MoE architectures fitting into real DevOps workflows? #DeepSeekMoE #AI #DevOps #LLM

AI is changing how DevOps teams learn and work. Our DevOps Engineer Eugene Kiselev, and resource manager Anna Boldyreva, share essential resources, trends, and tools for using AI in daily engineering tasks. Whether you’re new to AI or ready to build your own agents, this guide covers practical steps to get started safely: https://lnkd.in/d8qJNjpX #DataArt #DevOps #AI #MachineLearning #Learning Resources #CloudComputing

🔥10 AI Prompts Every Engineer Doing DevOps Should Know DevOps, Platform, and Eng Leaders: Stop drowning in drift, tickets, and late-night firefights. These prompts show how to: ⚡ Slash on-call noise ⚡ Automate RCA quickly ⚡ Close audit gaps before they burn you out 👉 If you’re not using them, you are missing out. https://bit.ly/3IRTfjf #DevOps #PlatformEngineering #AI #EngLeadership

⚡ AI Meets DevOps — My 10-Day Hands-On Journey Begins⚡ 🚀 Day 1 — AI + DevOps Challenge 🤖💻 Kicked off my 10-day AI + DevOps journey exploring how AI is reshaping modern DevOps workflows. 🔑 Focus Areas Today: ⚡ The role of AI in transforming DevOps practices ✨ Traditional AI vs. Generative AI in operations 🛠 Popular AI tools for DevOps engineers 💡 Built a Bash-based VM Health Check script to monitor CPU, memory, and disk usage — with an --explain flag for detailed insights (screenshot in repo 👇). 📂 Check out the code here: https://lnkd.in/dfWREkPE ✅ Key Takeaways: 1️⃣ Traditional AI excels at anomaly detection & forecasting using structured data. 2️⃣ Generative AI (LLMs) goes further — analyzing logs, explaining failures, and even suggesting auto-remediation. 3️⃣ Together, these approaches empower DevOps engineers to move from reactive monitoring to proactive and intelligent operations. ⚡ With just Day 1, it’s already clear: AI is a game-changer for automation, RCA, and operational visibility in cloud-native environments. #DevOps #AI #AIOps #CloudNative #Automation #OpenSource #Innovation

🌍 AI and DevOps: The Next Big Shift Every so often, engineering takes a leap forward. 👉 Agile changed how we plan. 👉 DevOps reshaped how we deliver. 👉 Now AI is starting to transform how we operate day to day. This isn’t just about shaving minutes off a deployment. Early adopters are already seeing: Faster recovery when things go wrong Smarter scaling that saves money Teams freed up to focus on building, not firefighting At Dynamasstech, we’re approaching AI in DevOps not as a shiny new tool, but as a shift in how engineering teams can work smarter and move faster. For tech leaders, the real question isn’t if this change is coming, but whether you’ll lead the way or play catch-up. 👉 Where do you see AI making the biggest dent in your teams over the next couple of years? #AI #DevOps #EngineeringLeadership

📊 AI in DevOps: The Reality Check AI has become a buzzword in DevOps—but the truth is, we’re still at the beginning of the journey. Many solutions today are point tools: useful for a specific step, but far from covering the whole SDLC. That doesn’t mean progress isn’t happening. We’re seeing promising advances like: ✔️ Reordering builds/tests to fail fast and save time ✔️ Natural language policy-as-code for easier governance ✔️ AI-driven remediation when pipelines break The takeaway? AI in DevOps isn’t hype—it’s just fragmented and unevenly useful. For enterprises working with big data pipelines, smart integration of AI will be key to reducing costs and accelerating analytics cycles. 🚀 The winners in this space will be the vendors (and teams) that blend AI seamlessly into workflows rather than bolting it on as a checkbox. #BigData #ArtificialIntelligence #DevOps

🔧 DevOps was never just about code. It’s about flow, speed, and resilience. But here’s the twist: AI is turning DevOps into something new — DevOps with cognition. Where I once worried about builds failing, I now wonder: ➡️ What if pipelines could self-heal? ➡️ What if monitoring systems didn’t just alert — but explain? ➡️ What if deployments weren’t just automated — but adaptive? That’s where the field is heading. And honestly, it’s wild to think that as engineers, we’re not just writing scripts anymore… We’re teaching machines how to reason. 💡 My two cents: DevOps + AI isn’t “coming.” It’s already quietly here. And it’s making the work less about fixing errors — and more about engineering intelligence into the system itself. #DevOps #AI #AIOps #CloudEngineering #MachineLearning #Automation #FutureOfWork #EngineeringExcellence