Foundational Machine Learning Operations (MLOps) for Beginners

Moving machine learning models from a local notebook to a reliable production environment is one of the biggest challenges in modern software engineering. This course introduces you to the core principles of Machine Learning Operations (MLOps), helping you build stable, automated, and scalable workflows. You will transition from writing isolated ML code to designing robust pipelines that automate training, deployment, and monitoring. Through clear explanations and structured text-based lessons, you will understand how to maintain model reliability in real-world scenarios. What you will learn: Understand foundational MLOps terminology, lifecycle stages, and core architectures; Configure automated machine learning pipelines for continuous training; Apply data and model versioning techniques to ensure reproducibility; Monitor production models for data drift and performance degradation; Establish basic CI/CD workflows tailored specifically for machine learning systems. The course begins with essential definitions and foundational MLOps concepts before guiding you through pipeline design, automation strategies, and post-deployment monitoring. This course is designed for aspiring ML engineers, data scientists, and software developers who are new to MLOps, with no prior DevOps or production deployment experience required. Start building reliable, production-ready machine learning systems today.