ML Deployment: Continual Learning and Feedback Loops

Deploying a machine learning model is only the beginning; keeping it accurate in a changing world is the real challenge. As real-world data shifts over time, static models degrade, making active monitoring and continuous updating essential for production success. This text-based course guides you through the foundational strategies needed to keep your production machine learning models accurate and reliable. You will transition from static deployments to dynamic, self-improving systems that adapt to new data without manual intervention. What you'll learn: Understand core concepts of model degradation, data drift, and concept drift in production; Establish robust feedback loops to capture real-world performance metrics; Implement periodic retraining pipelines and online learning strategies; Apply champion-challenger and shadow deployment strategies to safely test new models; Configure basic data drift detection and modern MLOps observability patterns; Practice designing automated workflows that trigger retraining based on performance drops. You will start by mastering the essential terminology of model drift and feedback loops before exploring practical retraining architectures. Through detailed written explanations and step-by-step system design exercises, you will learn how to design automated, resilient ML pipelines. This course is designed for aspiring MLOps engineers, data scientists, and software developers who are new to model deployment. No prior production deployment experience is required, though a basic understanding of machine learning concepts is helpful. Start reading today to build machine learning systems that continuously adapt and thrive in production environments.