MLOps Foundations: Automating, Optimizing, and Monitoring ML Models

Deploying a machine learning model is only the beginning; keeping it accurate and reliable in production requires continuous maintenance. Without proper automation and monitoring, model performance naturally degrades over time due to changing real-world data.\n\nThis text-based course guides you through the essential principles of Machine Learning Operations (MLOps). You will learn how to transition from static models to dynamic, automated systems that monitor performance, optimize resource usage, and trigger automatic retraining when accuracy drops.\n\nWhat you'll learn:\n- Understand foundational MLOps concepts and the lifecycle of production machine learning models\n- Implement automated retraining pipelines to keep models updated with fresh data\n- Detect data drift and concept drift before they impact your business metrics\n- Optimize model performance and footprint using quantization and pruning techniques\n- Configure continuous monitoring and alerting systems for real-time model health\n- Apply basic CI/CD principles to automate model deployment workflows safely\n\nThe course begins with key terminology and foundational concepts of model degradation before guiding you step-by-step through designing robust automation, optimization, and monitoring strategies. You will read through practical explanations and conceptual architectures that illustrate real-world MLOps workflows.\n\nThis course is designed for aspiring ML engineers, data scientists, and developers who are new to MLOps. No prior production deployment experience is required, though a basic understanding of machine learning concepts is helpful.\n\nStart reading today to build reliable, self-sustaining machine learning systems.