Nearly 80% of AI/ML projects end in failure. Surprising? In a world where AI is booming and solution blueprints are just a click away, building and deploying a machine learning model should be easy - right?

In reality, getting an AI solution into production and keeping it there is a minefield. That’s where MLOps comes in: a set of principles and practices designed to keep your project alive from proof of concept to scalable deployment. Ignore them, and your AI might never leave the sandbox.

This session dives into what MLOps really looks like in the wild. Through real-world “war stories” from refactoring a Computer Vision system on Azure - deployed on edge devices in pharmaceutical plants - you’ll learn why AI systems fail, how to avoid technical debt, and what practices are essential for operationalizing ML at scale. If you're working with AI on Azure, this session will help you connect theory to production reality.