MLOps Foundations: Deploying and Monitoring Machine Learning in the Cloud

Building a machine learning model is only the first step; the real challenge lies in deploying, scaling, and maintaining it in a production environment. Many aspiring data scientists struggle to bridge the gap between a local notebook and a reliable, automated cloud system. This text-only course provides a clear pathway to understanding and applying Machine Learning Operations (MLOps). You will transition from writing basic model code to designing automated pipelines that handle data validation, model training, deployment, and real-time performance tracking. What you'll learn: Understand the fundamental concepts of MLOps and the lifecycle of production machine learning systems; Configure automated pipelines for continuous integration and continuous delivery (CI/CD) of ML models; Implement model monitoring and observability strategies to detect data drift and performance decay; Apply containerization basics to package models consistently for cloud deployment; Manage model versions and metadata using a centralized model registry. The course begins with foundational terminology, comparing traditional DevOps with MLOps, before guiding you through data pipeline design, model deployment patterns, and post-deployment monitoring strategies. You will read structured explanations and analyze real-world architecture scenarios to build your cloud deployment skills. This course is designed for beginner data scientists, software engineers, and system administrators looking to enter the field of MLOps, starting with the absolute basics and requiring no prior experience with cloud operations. Start reading today to build reliable, scalable machine learning systems in the cloud.