DevOps 2023: From Assistants to Intelligent Platforms

This list is spot on. Companies must not get stuck in the past and align their requirements accordingly. DevOps Engineers should focus their efforts on getting skilled up in those area to be able to keep up with the times.

🚀 DevOps Engineers: Your AI Just Got 10x More Powerful in 2026 Most people are using AI like a chatbot. But top DevOps engineers? They’re turning it into a full-fledged engineering assistant using Claude Code plugins. Here’s the real game-changer 👇 --- 🔥 The DevOps Plugin Stack You Should Be Using 🧠 Foundation (Start Here) Context7 → Live documentation (no outdated Terraform/K8s configs) Security Guidance → Finds vulnerabilities before production does 💡 This alone saves hours of debugging + prevents costly mistakes. --- ⚙️ Infrastructure & IaC Mastery HashiCorp Agent Skills Terraform best practices Module refactoring Testing automation Shipyard Validates Terraform, Docker, K8s, Ansible in one go 👉 Think: “terraform validate” on steroids. --- ☸️ Kubernetes + SRE Toolkit K8s Troubleshooter Fix OOMKilled, CrashLoopBackOff instantly Monitoring & Observability Prometheus, Grafana, OpenTelemetry setups CI/CD Optimization Reduce pipeline time from 12 mins → 5 mins --- 💸 FinOps & Cost Optimization Detect unused resources Right-size infra Optimize AWS spend automatically --- 🔗 GitHub Plugin = DevOps Command Center Manage PRs across repos Debug CI failures Automate workflows --- ⚡ Real Impact (Not Hype) With the right plugins: 🕒 Debugging time ↓ 60% 🔐 Security issues caught BEFORE deployment ⚙️ CI/CD pipelines optimized automatically ☸️ K8s issues resolved with structured playbooks --- 🧩 Recommended Setup (DevOps Generalist) claude plugin install context7 claude plugin install security-guidance claude plugin install github claude plugin marketplace add devops-claude-skills claude plugin install iac-terraform@devops-skills claude plugin install k8s-troubleshooter@devops-skills claude plugin install ci-cd@devops-skills --- 💭 Reality Check AI won’t replace DevOps engineers. But DevOps engineers using AI plugins will replace those who don’t. --- 📌 Pro Tip Start with: 👉 Context7 👉 Security Guidance Then expand based on your role. --- 💬 Curious — which tool are you using daily? Terraform | Kubernetes | CI/CD | AWS | All of them? --- #DevOps #PlatformEngineering #ClaudeCode #Kubernetes #Terraform #Cloud #AI #Automation #DevSecOps #FinOps#

Your Complete DevOps + AI Roadmap for 2026 The DevOps engineer of tomorrow isn’t just shipping code, they’re deploying intelligence. Here’s the structured path from foundations to AI-powered infrastructure: ⿡ Linux Fundamentals Master commands, file systems, and networking. Everything runs on Linux — this is non-negotiable. ⿢ Shell Scripting Automate the boring stuff first. Every script you write sharpens your instincts for what should and shouldn’t be manual. ⿣ Git & GitHub Version control isn’t just for code anymore,it’s for infrastructure, configs, and ML models too. ⿤ CI/CD Pipelines Jenkins, GitHub Actions, GitLab CI. Learn to automate builds, tests, and deployments end to end. ⿥ Docker Containerization is the bridge between “works on my machine” and “works everywhere.” ⿦ Kubernetes Orchestrate at scale. This is also where AI workloads live — GPU node pools, resource quotas, model serving. ⿧ Ansible Make your infrastructure reproducible. Configuration drift kills production systems. ⿨ Terraform Define everything as code. Cloud resources, networking, permissions ,all version controlled. ⿩ Cloud Platforms AWS, Azure, or GCP. Pick one, go deep. AI services are baked into all three now. - Python The glue of DevOps and AI. Automation scripts, API integrations, and ML pipelines all run on it. - AI/ML Operations (MLOps) The New Frontier This is where DevOps gets its next evolution: → Model Deployment: Serve models with TorchServe, TF Serving, or FastAPI behind a K8s ingress → MLflow & Kubeflow: Track experiments, version models, and build reproducible ML pipelines → GPU Provisioning: Manage node selectors, tolerations, and resource limits for GPU workloads → Model Monitoring: Detect drift, track latency, and alert on prediction degradation in production → Feature Stores: Feast or Tecton for consistent feature serving between training and inference → LLMOps: Prompt versioning, fine-tune pipelines, and cost monitoring for large language models Why this matters right now: Companies are drowning in AI models that never make it to production. DevOps engineers who can bridge the gap between data science and production infrastructure are the most sought-after people in tech. Pro tip: Don’t rush. Each layer builds on the last. Hands-on practice beats theory every time — spin up a real project at each step. What step are you on right now? Drop it below hashtag #DevOps hashtag #MLOps hashtag #LLMOps hashtag #CloudComputing hashtag #Kubernetes hashtag #Terraform hashtag #AWS hashtag #Docker hashtag #Python hashtag #AIEngineering hashtag #DevOpsRoadmap

CI/CD vs. GitOps vs. MLOps: Understanding the Modern Engineering Stack Navigating the world of DevOps can feel like wading through an alphabet soup of acronyms. While they all aim to automate and improve the software lifecycle, they solve very different problems. Here is a quick breakdown of how these three heavyweights compare: 🔵 CI/CD: The Foundation of Speed CI/CD (Continuous Integration/Continuous Deployment) is the engine of modern software development. It focuses on the application code. • The Goal: Move code from a developer's laptop to production as fast and safely as possible. • Key Steps: Automated testing (Unit/Integration), Security scanning (SAST), and building artifacts (Docker images). • The Vibe: "Is my code broken? No? Okay, ship it." 🟢 GitOps: The Source of Truth GitOps is an evolution of Infrastructure as Code (IaC). It uses Git as the single source of truth for your infrastructure and cluster state. • The Goal: Ensure the environment (Kubernetes) matches exactly what is defined in your repository. • Key Steps: Declarative manifests (Helm/Kustomize), drift detection, and automated reconciliation via tools like ArgoCD or Flux. • The Vibe: "If it’s not in Git, it doesn't exist in the cluster." 🔴 MLOps: The Data Challenge MLOps brings DevOps principles to Machine Learning. Unlike standard code, ML models are living things that depend on shifting data. • The Goal: Manage the lifecycle of models, ensuring they remain accurate and unbiased over time. • Key Steps: Data validation, Hyperparameter Tuning (HPO), Model Registration, and monitoring for Data Drift. • The Vibe: "The code is fine, but the data changed—time to retrain." Which one do you need? The truth is, most high-performing teams use all three. CI/CD builds the app, GitOps manages the environment where it lives, and MLOps ensures the "intelligence" inside the app stays sharp. Which part of the pipeline do you find most challenging to automate? Let’s discuss in the comments! #DevOps #MLOps #GitOps #CICD #SoftwareEngineering #CloudNative #Kubernetes #DataScience

🚀 From Pipeline Chaos to AI-Powered Flow: How Anthropic Claude Transformed Our DevOps CI/CD (Real Case Study) Hey LinkedIn DevOps & Cloud crew 👋 Ever had a CI/CD pipeline that felt like a black hole? 😩 The Nightmare Problem: At our AWS-heavy enterprise (think multi-team, Terraform-heavy deploys), flaky tests were killing us. 40% of engineer time wasted on debugging vague pytest failures and log gremlins. 100+ daily deploys? Delays everywhere, frustrated squads, and FinOps alerts spiking from idle runners. Sound familiar? The Game-Changing Fix: We plugged Anthropic’s Claude straight into GitHub Actions + Jenkins. Here’s the magic: 🔹 Test Fail? Claude Analyzes: Parses logs, diffs, stack traces in <10s. 🔹 Instant Insights: Outputs root cause + fix code (e.g., “Update Terraform null_resource dependency”). 🔹 Auto-Action: Generates PRs, pings Slack with squad-routed verdicts. Prompt example: “Debug this [log + diff]. Suggest Python/Terraform fix.” Results? 70% less manual triage, 2x faster pipelines, happier teams. Bonus: FinOps savings on compute! 💰 3 Key Takeaways for Your Toolkit: ✅ LLMs like Claude = Your new DevOps sidekick for grunt work. ✅ Integrate via API in post-test hooks—low risk, high ROI (~$0.01/analysis). ✅ Secure it: IAM roles + prompt guardrails. DevOps leaders, FinOps pros, AWS architects: • Using AI in pipelines yet? What’s your stack? • Who’s battled flaky tests? Share war stories! • Let’s connect if you’re optimizing CI/CD or cloud costs—always up for a chat. 🤝 #DevOps #CICD #AnthropicClaude #AWS #CloudEngineering #AIinDevOps

🚀 Introducing DevOpsToolkit — Built for Engineers, by an Engineer After intense building, refining, and pushing boundaries… I’m excited to finally share something I’ve been working on: 👉 A unified DevOps & Platform Engineering toolkit designed to simplify, accelerate, and automate everyday engineering workflows. 🔧 What it brings: • Ready-to-use DevOps scripts (Bash, Python, PowerShell) • Kubernetes & Docker generators (YAML, Helm-ready) • CI/CD pipeline builders (Jenkins, GitHub Actions, GitLab, more) • Cloud-ready configurations (multi-provider mindset) • Security, observability, and automation utilities • Smart AI-powered assistance (early stage, evolving fast) 💡 Built with a simple idea: Instead of searching, rewriting, and debugging the same things again and again… 👉 Why not have everything in one place, ready to use? ⚡ What’s coming next: • BYOK (Bring Your Own Key) for LLM integrations • DevOps command simulation (learn by seeing what happens internally) • Intelligent tool recommendations This is just the beginning — the vision is much bigger: ➡️ A self-evolving DevOps ecosystem with thousands of tools and generators. 🌐 Try it here: https://devopstoolkit.dev/ Would love your feedback, ideas, and brutal honesty 🙌 Let’s build something powerful together. DEVOPS INSTITUTE Agentic DevOps DevOpsCube DevOps Learner Community IBM Amazon Web Services (AWS) #DevOps #PlatformEngineering #Kubernetes #Docker #Cloud #Automation #AI #SRE #DevSecOps #ibmchampion #devopsinstitute #peoplecertambassador #gitlabcertified #devopstoolkit #devopstoolkit.dev

𝗠𝗼𝘀𝘁 𝗗𝗲𝘃𝗢𝗽𝘀 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝗺𝗮𝘀𝘁𝗲𝗿 𝘁𝗼𝗼𝗹𝘀. 𝗧𝗵𝗲 𝗲𝗹𝗶𝘁𝗲 𝟭% 𝗺𝗮𝘀𝘁𝗲𝗿 𝘁𝗵𝗲 𝗲𝗻𝘁𝗶𝗿𝗲 𝟭𝟲-𝘀𝘁𝗮𝗴𝗲 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗷𝗼𝘂𝗿𝗻𝗲𝘆. As Werner Vogels, CTO of Amazon, once noted: "Resilient systems are not built overnight, they are engineered through deliberate, layered expertise." The DevOps landscape has shifted from a niche discipline to the backbone of modern software delivery. Organizations that treat it as a checklist fail. Those who treat it as a mastery journey win. Here is the complete 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 𝗲𝘃𝗲𝗿𝘆 𝗗𝗲𝘃𝗢𝗽𝘀 𝗽𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗻𝗲𝗲𝗱𝘀: 𝟭. 𝗩𝗲𝗿𝘀𝗶𝗼𝗻 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 & 𝗔𝗜-𝗔𝘀𝘀𝗶𝘀𝘁𝗲𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁  Highlighted by Kelsey Hightower (Google), mastering Git workflows combined with AI coding tools is now the non-negotiable foundation of modern engineering teams. 𝟮. 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 & 𝗦𝗰𝗿𝗶𝗽𝘁𝗶𝗻𝗴  Python, Go, and Bash remain the core languages driving automation, as Tanya Reilly consistently emphasizes in her systems engineering frameworks. 𝟯. 𝗖𝗜/𝗖𝗗 & 𝗚𝗶𝘁𝗢𝗽𝘀 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀  Continuous integration and GitOps workflows dramatically reduce release friction. Charity Majors of Honeycomb has long advocated for pipeline maturity as the heartbeat of delivery culture. 𝟰. 𝗖𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗣𝗼𝗹𝗶𝗰𝘆 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁  Tools like Ansible enforce consistency at scale. Mitchell Hashimoto built an entire ecosystem around this principle with HashiCorp. 𝟱. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝘀 𝗖𝗼𝗱𝗲 (𝗜𝗮𝗖)  Terraform and CloudFormation redefine how teams provision environments, a shift Kief Morris documented extensively in his foundational IaC work. 𝟲. 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻  Kubernetes has become the operating system of the cloud era. Brendan Burns, its co-creator, architected the very logic behind scalable container management. 𝟳. 𝗖𝗹𝗼𝘂𝗱 & 𝗠𝘂𝗹𝘁𝗶-𝗖𝗹𝗼𝘂𝗱 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆  Gregor Hohpe's enterprise architecture thinking applies directly here: design for portability across AWS, Azure, and GCP before you need it. 𝟴. 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Docker democratized applicaKelsey Hightowertion packaging. Solomon Hykes redefined how teams ship software consistently across environments. 𝟵. 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗔𝗜𝗢𝗽𝘀  Cindy Sridharan's work on distributed systems observability shows that monitoring logs, metrics, and traces is not optional, it is survival. 𝟭𝟬. 𝗗𝗲𝘃𝗦𝗲𝗰𝗢𝗽𝘀 & 𝗦𝘂𝗽𝗽𝗹𝘆 𝗖𝗵𝗮𝗶𝗻 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆  Shannon Lietz pioneered DevSecOps, proving security integrated early costs a fraction of security bolted on late. #InvensisLearnining #DevOps #DevOpsRoadmap #PlatformEngineering #SRE #CloudEngineering #GitOps #AIOps #DevSecOps #InfrastructureAsCode #Kubernetes

As organizations accelerate their transition to cloud-based application delivery and #agile #DevOps, automated testing has become a vital part of maintaining quality at speed. As development teams adopt agentic coding tools such as Kiro, Cursor, #Cline and Windsurf, the testing bottleneck has only become greater. However, traditional #testautomation often requires heavy scripting by human developers, result in brittle tests that require significant maintenance effort, and lack the intelligence needed to support evolving applications. In this article, you will learn how Rapise - Scriptless Test Automation by Inflectra uses #AgenticAI powered by Amazon Web Services (AWS) #Bedrock to automate test creation, adapt to application changes, and reduce QA cycle times by up to 70%. 💥 #bettertogether #partnership https://lnkd.in/e6F7mVCH

Not every DevOps skill is worth learning in 2026. These 5 moved the salary needle. The rest are already commodity. Here's what changed: Kubernetes hit 82% production adoption (CNCF 2025 Annual Survey). The 2022 winners, Docker, basic Terraform, generic Jenkins, are now floor skills. You need them. Nobody pays a premium for them anymore. The 5 that still pay: 1. Kubernetes + GitOps 77% of orgs have adopted GitOps to some degree. ArgoCD and Flux are table stakes for any senior platform role. 2. Platform engineering + Backstage Gartner forecasts 80% of large software orgs will have dedicated platform teams by end of 2026. IDP experience pays 20 to 30 percent above generic DevOps. 3. IaC security (Checkov + Trivy) 80M+ Checkov downloads. The scarce skill isn't running the scanner. It's wiring policy-as-code into the pipeline without blocking devs. 4. Observability with OpenTelemetry Second-fastest-growing CNCF project, 24K+ contributors. Teams want portable instrumentation, not vendor lock-in. 5. FinOps + Kubecost 98% of FinOps teams now manage AI spend (up from 63% last year). Container cost attribution is the new line item on every quarterly review. The 8-slide breakdown with per-skill salary premiums is in the carousel. Save it for your next pay review. Then settle this for us: Which one are you doubling down on, and which one on this list is the most overrated in your stack? One of each, in the comments.

Your developers are spending more time fighting infrastructure than shipping features. That's not a people problem. That's a scale problem. I've been thinking about why global engineering teams — with hundreds of smart developers — still take weeks to release a single feature. The answer? Traditional DevOps wasn't built for this. Here's what the data says 👇 The State of Platform Engineering Report Vol. 4 (518 engineers surveyed globally) confirms what many of us already feel on the ground: 📊 94% of organizations now view AI as critical to the future of platform engineering 📊 55.9% of companies now run more than one platform — by intentional design 📊 Nearly 30% of platform teams don't measure success at all — and it's killing their ROI So what's actually changing in 2026? 𝗗𝗲𝘃𝗢𝗽𝘀 𝘄𝗮𝘀 𝗿𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗮𝗿𝘆. It broke silos. It gave us CI/CD, IaC, and shared ownership. It works beautifully — for small to mid-sized teams. But at global scale? → Every team picks different tools → tool sprawl → Same problems get solved 10 times over → GDPR, compliance, data sovereignty become nightmares → Developers burn out managing infrastructure instead of building products 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝘁𝗵𝗲 𝗮𝗻𝘀𝘄𝗲𝗿. Think of it this way: 🍳 DevOps = every team runs their own kitchen 🏭 Platform Engineering = one professional central kitchen with ready tools, standard recipes, and built-in safety Developers stop worrying about setup and just focus on building great features. What a Platform team actually delivers: ✅ Self-service environments (minutes, not weeks) ✅ "Golden Paths" — safe, standardized workflows ✅ Security & compliance baked in by default — what the report calls "shifting DOWN" not just left ✅ A clean developer portal for full self-service The result? Guided freedom instead of chaos. If you're an engineer in 2026, here's your roadmap: 1️⃣ Master DevOps fundamentals — Docker, Kubernetes, Terraform, CI/CD 2️⃣ Level up to Platform Engineering — IDPs, Backstage, DevEx mindset 3️⃣ Build even a small internal platform project — massive interview differentiator DevOps isn't dead. But the companies winning in 2026 are building Platform Engineering ON TOP of it. The future is DevOps made effortless through smart platforms. 🚀 📄 I'm sharing the full State of Platform Engineering Report Vol. 4 below — free, no paywall. 518 engineers. Real data. Worth a read. 💬 What's the biggest time-waster in your current DevOps setup? Drop it below 👇 #DevOps #PlatformEngineering #Kubernetes #CloudEngineering #DeveloperExperience #SoftwareEngineering #TechIn2026 #IDP #BackStage #DevEx