MLOps Roadmap: A Practical Path from Beginner to Production

If you’re a data scientist tired of models dying in notebooks, a junior ML engineer wondering what “production-ready” actually means, or a DevOps engineer curious about this MLOps thing everyone’s hiring for — this article is for you. This is an mlops roadmap for beginners that also works for mid-level engineers planning their next career move. I’ve shipped ML models to production across fraud detection, demand forecasting, and support ticket classification systems. What I’m sharing here isn’t theory — it’s what actually works when you need machine learning models running reliably at 3 AM without waking anyone up. Here’s what you’ll learn in this article: What MLOps actually covers in practice (not just “deploying models”) How to read an mlops roadmap diagram and translate it into a learning plan A complete mlops skills roadmap organized by experience level A concrete 30/60/90-day mlops learning roadmap with real deliverables The devops to mlops roadmap for engineers transitioning from