Foundations of MLOps and AI Infrastructure

Transitioning a machine learning model from a local notebook to a reliable production environment requires a specialized set of practices. This text-based course guides you through the foundational concepts of Machine Learning Operations (MLOps) and the infrastructure that powers modern AI systems. You will learn how to bridge the gap between data science and system engineering, designing automated pipelines, managing model versioning, and establishing robust monitoring systems to ensure your models perform consistently in real-world scenarios. What you'll learn: * Understand the core terminology of MLOps, model lifecycles, and AI infrastructure components. * Configure continuous integration and continuous delivery workflows tailored for machine learning assets. * Track experiments and manage model versioning using industry-standard tools like MLflow. * Implement containerization basics with Docker to package models for consistent deployment. * Deploy machine learning models as scalable APIs and monitor their performance for data drift. * Explore modern LLMOps concepts, including infrastructure requirements for serving large language models. The course begins with essential terminology and architectural fundamentals before guiding you through step-by-step written tutorials on pipeline automation, containerization, and model monitoring. You will practice these concepts through conceptual exercises and structured code walkthroughs. This course is designed for aspiring MLOps engineers, data scientists, and software developers who are new to machine learning operations. No prior infrastructure or DevOps experience is required. Start reading today to build a strong foundation in production-grade AI systems.