MLOps in a Nutshell

Think of MLOps as the behind-the-scenes wizardry that turns a funky machine-learning prototype into a rock-solid production service—no more “it worked on my laptop” dramas. This video unpacks why MLOps is a must (hello, banking fraud detection!), how it bridges the gap between data science and DevOps, and what real-world headaches it solves when you’re pushing models live.

You’ll get the lowdown on containerization, CI/CD pipelines, automated monitoring, data-drift detection, plus the go-to toolset (Docker, Kubernetes, MLflow, Prometheus, Grafana, and more). By the end, you’ll see a full MLOps workflow from raw data to deployed model—and know exactly why data scientists, DevOps/cloud engineers, and even engineering managers should care.

Watch on YouTube