In this short, I’ll break down the three most important deployment pipelines every DevOps, Cloud, or MLOps engineer should understand: Traditional CI/CD, GitOps, and MLOps pipelines. I’ve explained how push-based CI/CD works, how GitOps uses Git as the source of truth with tools like Argo CD, and how MLOps pipelines validate data, retrain models, benchmark performance, and safely deploy AI systems. Master all three if you want to stay relevant in this Cloud/AI era. Instagram: https://www.instagram.com/vishakha.sadhwani LinkedIn: https://www.linkedin.com/in/vsadhwani/ Subscribe to my Newsletter: https://www.tech5ense.com/ ----------------------------------------------- 🙋♀️ ABOUT ME I’m Vishakha ☁️; A Cloud Architect and DevOps enthusiast who loves turning complex cloud concepts into simple, practical insights you can actually use. You’ll find real-world projects 💻 quick explainers ☕ and honest career advice 💬; From Kubernetes and Terraform to landing roles and building your tech brand, I keep things approachable, actionable, and a little fun so learning Cloud and DevOps actually feels exciting. If this sounds like your vibe, hit subscribe, and let’s keep growing together 🚀 ----------------------------------------------- 🏷️ TAGS vishakha sadhwani,cloud computing,learn to code,cicd pipeline,gitops explained,mlops pipeline,devops interview questions,gitops vs ci cd,ci cd tutorial for beginners,mlops for beginners,gitops tutorial,mlops interview questions,backend interview questions,interview questions and answers,devops roadmap 2026,cloud engineer,software engineer,ai engineer,full stack developer,software development,web development,cloud interview questions ----------------------------------------------- #️⃣ HASHTAGS #coding #devops #interviews
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Very informative
Neatly explained vishakha
Make tutorials
MLOps significantly streamlines the processes of training, validation, deployment, and retraining on new data. However, these workflows primarily assume that the underlying data distribution remains relatively stable. When the data distribution shifts drastically, incremental retraining may no longer be sufficient. In such cases, the problem may require revisiting the modeling approach itself—potentially selecting a different algorithm, redefining features, or even reformulating the problem. MLOps facilitates iteration, but it does not eliminate the need for fundamental changes when the data landscape changes significantly.
Thank you
Super
Valuable info thanks!
VERY GOOOD
🫶
Valuable content ✅
Thank you ❤ eagerly waiting for more info like this
Good information 👍
First comment