DevOps Bulletin’s Post

🚀 Project 1: MLOps Starter I explored how DevOps principles can be applied to AI/ML workflows. Using a small dataset, I built a simple ML model that converts text into numbers for predictions and wrapped it in a lightweight Flask API. Now, the model can respond to POST requests in real time, giving predictions instantly. This project gave me hands-on experience in model deployment, API serving, and the foundations of MLOps — key skills for building scalable and production-ready AI solutions. Github : https://lnkd.in/gT22tUTY #MLOps #MachineLearning #Flask #DevOps #AI #API #Womeninstem #LearningInPublic #Womenintech

Day 6 of learning AI + DevOps 🚀 Today I connected all the pieces and understood how a RAG system works end-to-end. There are two main parts: 👉 Data preparation (offline): load data split into chunks convert to embeddings store in vector database 👉 Query time (online): user asks a question convert it into embedding search vector database retrieve relevant chunks send to model generate answer It’s like combining: → search (to find relevant info) → AI (to generate the answer) Feels great to finally see the full picture of how modern AI systems are built. Next step: trying to build a small project around this. #AI #DevOps #LearningInPublic

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

GitHub's adding Copilot review metrics to their API. Finally, we can start proving our AI overlords are actually helping us ship code faster. 🚀 #DevOps #AI

In my recent projects, I’ve been exploring ways to deploy PyTorch models efficiently in production, and TorchServe has been a game-changer. TorchServe is a framework designed to serve PyTorch models at scale. It allows you to deploy trained models as APIs quickly, handle multiple models at once, and manage versions seamlessly. What I found particularly valuable: Easy model deployment: Package and serve models without writing complex serving code Scalability: Handles high volumes of requests efficiently Multi-model support: Serve multiple models simultaneously and manage versions Metrics & logging: Built-in monitoring for model performance in production For companies building AI products, TorchServe makes it much easier to move from research to production without heavy DevOps overhead. I’m curious—how are others deploying PyTorch models in production? Are you using TorchServe, or do you prefer custom solutions? #AI #MachineLearning #PyTorch #TorchServe #MLOps #ModelDeployment

Sunday thought. AI governance isn’t a checkbox exercise. Just had a great discussion with Roman Trofimov about the recent Claude token-burning story circulating on Reddit. One developer reported the system making dozens of tool calls in a single turn, consuming large amounts of tokens while exploring context. What I like most is how our team reacts. No waiting for formal reviews. People raise signals, we discuss, and the system gets tuned. Self-organizing teams are the best control system for fast-moving AI. Looking forward to continuing this tomorrow with the DevOps team. #AIGovernance #Strikersoft #DevOps #SelfOrganizingTeams

Most companies are buying AI coding tools. Few are thinking about what comes after. When 80%+ of your code is written by AI (Gartner's prediction, not mine), your bottleneck won't be writing code — it'll be shipping it safely, quickly, and with visibility into what's actually happening. That's the gap we solve at Opsera. Our Hummingbird AI doesn't just watch your pipelines — it reasons across them. It tells you why a deployment failed, what to do next, and self-heals before your on-call engineer even gets paged. The teams winning the AI era aren't just using Copilot. They're orchestrating everything that happens after the code is written. Curious how your current DevOps stack would hold up? Happy to walk through it. 👇 #DevOps #AIAgents #SoftwareDelivery #Opsera

Nobody told me the first two weeks with AI are supposed to feel useless. I figured that out the hard way. Post 18 of the AI + DevOps series. Most engineers I know have tried AI tools at least once. Most quietly stopped after two weeks. Not because the tools weren't good — because nobody told them the frustrating middle exists. You're not getting value yet in those first two weeks. You're building the habit of reaching for it at the right moment. That's the whole invisible work nobody posts about. The engineers who stuck with it didn't have better tools or more time. They kept going through the part that felt pointless until one day it didn't. I remember the exact week it clicked. Not because I found a better prompt. Because I stopped treating it like a shortcut and started treating it like a colleague I needed to break in. The frustrating middle is the whole thing. Most people just don't tell you it exists. Did you push through the frustrating middle with AI — or are you still in it? 👇 Drop it below. No wrong answer. #AI #DevOps #CloudEngineering #Productivity #SoftwareEngineering #RealTalk #AIAdoption #TechLife #EngineeringLife #AITools

AI isn't replacing DevOps engineers. It's exposing the ones who stopped learning. The pace of AI in infrastructure is brutal right now. In the last 18 months: AI-assisted code review, auto-remediation pipelines, LLM-powered incident triage, AI writing your Terraform, your runbooks, your PRDs. And most teams are still arguing about whether to allow AI tools at work. That gap — between what's possible and what teams are actually doing — is the real problem. Not the AI. The engineers who'll struggle aren't the ones AI can automate. They're the ones who opted out of keeping up. I've been experimenting with integrating AI into my own DevOps workflows — not to replace judgment, but to compress the boring parts and spend more time on decisions that actually matter. The tools are moving fast. The question is whether you're moving with them. #DevOps #CloudEngineering #AI #Infrastructure #CareerGrowth

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