AI ROI Hinges on DevOps Fundamentals

MLOps best practices for scaling AI products Did you know that up to 85% of AI projects never make it to production? The culprit isn't a lack of innovative models, but rather the difficulty in scaling and maintaining them. That's where MLOps comes in – your key to unlocking AI's true potential. MLOps isn't just DevOps for machine learning; it's a cultural and engineering shift focused on automating and monitoring the entire AI lifecycle. Think streamlined deployments, continuous integration/continuous delivery

Kubernetes orchestrates containers. Agentic AI orchestrates thought. Here is the Architecture 👍 We have moved way beyond "ask AI → get answer." Meet Agentic AI - AI that doesn't just respond, it thinks, plans, and acts on its own. Here's how it works in plain English 👇 🧠 The 3 Pillars of a True AI Agent: → Autonomy — acts independently, no hand-holding → Agency — has a goal and chases it → Asynchronicity — works in the background, event-driven 🔄 The Loop that powers it all: Perceive → Reason → Act → Repeat It reads the world, thinks through it, takes action via tools & APIs, and loops back — non-stop. 💾 It even has memory (3 types!): Working — what's happening right now Episodic — what happened before Semantic — deep knowledge it always carries The bottom line? We're moving from single-turn chatbots to collaborative AI swarms — teams of specialized agents working together like a well-oiled DevOps pipeline. 🚀 And with Governance-by-Construction (RBAC, sandboxed execution, audit logs), safety is built in - not bolted on. #AgenticAI #DevOps #CloudNative #AI2026 #MachineLearning #Innovation #PlatformEngineering #TechTrends

AI in DevOps isn’t failing, it’s blind. Harness is fixing that. By bringing full context into software delivery, it turns scattered data into clear decisions, faster fixes, and smarter AI that actually understands systems. Read More: https://lnkd.in/gfyj7x_W #DQChannels #harnessAI #sofyware #AI

Folks, is DevOps fit for purpose in an AI world? Spoiler, yes but with a caveat - if we move from pipelines to systems of intent. Martin Fowler, Patrick Debois, Gene Kim et al gave us the cultural foundation; AI now lets DevOps anticipate, self‑optimize and translate business goals into safe, executable plans. Read about the future of DevOps in full the full article here: 👉 https://lnkd.in/eA7bwHGB All based on themes from my new book: A Brief History of Engineering. #DevOps #AI #SystemsOfIntent #SRE #EngineeringLeadership OTTRA Limited

🚀 AI can generate code at lightning speed — but your CI/CD pipeline might be slowing everything down In this session with Arne Blankerts, you’ll learn how to adapt your delivery pipeline to AI-accelerated development: 🧠 Understand why LLM-generated code can overwhelm traditional CI/CD 💪 Strengthen early feedback loops and shift signals left 🔨 Turn your pipeline back into a driver of speed, not a bottleneck ⚡Learn how to keep up with modern development workflows and make your CI/CD process fit for the age of AI. 📅 Tuesday, June 9th, 26 | 🕘 13:45 - 14:30 | webinale | 📍Berlin 👉 Check out the session: https://lnkd.in/dEYi8E_t #webinale #AIDevelopment #LLM #CICD #DevOps #SoftwareDevelopment

From containers to intelligence—learn why today’s smartest companies are building on cloud-native AI platforms. Insightful read from DevOps University.  🔍 https://lnkd.in/g5mjTAWj  #AIPlatforms #CloudComputing #Upskilling

GitOps changed how we manage applications, but what happens when it meets GenAI? Managing LLM pipelines is not just about deploying models; it is about handling complexity, versioning, observability, and continuous updates at scale. This is where things can quickly become messy without the right approach. In this session, Ravindra Verma explores how GitOps and ArgoCD can be extended to manage LLM pipelines effectively. It is a practical take on bringing structure, automation, and reliability to AI workflows using tools we already trust in the cloud native ecosystem. If you are working with Kubernetes, AI, or platform engineering, this is a session you should definitely catch. May 2, 2026 (Sat) CogNerd #Kubernetes #GitOps #GenAI #ArgoCD #PlatformEngineering #CloudNative #Kubesimplify

Many organisations are investing heavily in AI but are still struggling to move models from pilot into production and scale them effectively. In this clip from our recent MLOps & DevOps webinar, Jonathan Ede shares insights from recent industry reports on the current state of AI in the enterprise and why MLOps has become more important than ever in turning experimentation into real, scalable impact. Download the full session here: https://lnkd.in/eJAwaQHe #EnterpriseAI #AIInProduction #MLOps #DevOps #AIAtScale

Bridging the Gap: From DevOps to MLOps 🚀 I recently wrapped up an intensive MLOps session with Scaler, and the transition from traditional software engineering to operationalizing AI is fascinating. The Key Shift: In DevOps, we manage code. In MLOps, we manage the intersection of code, data, and models. The complexity scales significantly with Data Drift and automated retraining. My Strategy: I’ll be integrating these principles into my work with real-time data streaming—moving from "Data in Motion" to "Intelligence in Production" by building self-healing, automated ML pipelines. Motivation: This session bridged the "engineering gap" for me. It’s a perfect launchpad as I prepare for my AWS GenAI certifictions. Onward to making AI scalable and production-ready! 🎓 #MLOps #Scaler #GenerativeAI #DataEngineering #CloudNative #ContinuousLearning

A great takeaway from James Brookbank: “AI isn’t making everyone better—it’s making strong teams faster.” This is a good reminder that AI amplifies what’s already there. Watch the full session from the DevOps Modernization Summit, hosted by Harness, on demand: https://lnkd.in/eQGyj4xd #DevOps #AI #PlatformEngineering