MLOps and Big Data Foundations: Deploying Machine Learning Models

Building a great machine learning model is only half the battle; the real challenge lies in deploying, scaling, and maintaining it in a production environment. Bridging the gap between data science and systems engineering is essential for delivering real-world business value. This text-based course guides you through the core concepts of MLOps and big data engineering. You will transition from writing isolated model code to understanding how automated pipelines, containerization, and data scaling work together to keep models running reliably in production. What you'll learn: - Understand the core lifecycle of MLOps, from data ingestion to model deployment and monitoring - Explore big data processing concepts and how distributed systems handle massive datasets - Learn the fundamentals of containerization to package models consistently for any environment - Configure basic model registries to track, version, and manage your machine learning assets - Apply continuous integration and continuous delivery concepts to automate model deployments - Implement basic monitoring strategies to track model performance and detect data drift over time You will start with fundamental definitions and MLOps terminology before moving step-by-step through data pipelines, containerization, and deployment workflows. Through clear written explanations and practical code scenarios, you will build a solid conceptual and practical foundation. This course is designed for aspiring data scientists, software engineers, and system administrators who want to learn MLOps from scratch. No prior experience with deployment pipelines or big data infrastructure is required. Start reading today to take your machine learning models out of the notebook and into the real world.