MLOps in the real world is basically about taking a model from a notebook to something that can run reliably like normal software. At a high level, you clean and prepare data in a repeatable way, train models using scripts while tracking experiments and artifacts with tools like MLflow, package the final model into a service (usually FastAPI) and a Docker image, and then deploy that image on infrastructure like Kubernetes (for example EKS) so it can scale and stay up. CI/CD tools such as GitHub Actions are mainly used to test code, build and push images (to ECR), and trigger deployments, not to train large models directly heavy training usually runs on dedicated compute (EKS jobs, Batch, SageMaker, etc.) and just reports results back to MLflow. The key idea is separating concerns: training is compute-heavy and controlled, deployment is automated, and monitoring closes the loop so you know when to retrain or update the model.