A/B Testing and Online Experimentation for Feed Ranking Systems

Recommender systems and social media feeds rely on complex machine learning models to keep users engaged, but how do you know if a new model actually improves the user experience? Online experimentation is the industry-standard way to safely validate algorithm updates before a full-scale rollout. This text-based course guides you through the fundamentals of setting up, running, and analyzing online experiments specifically for feed ranking. You will learn how to balance user engagement metrics against system latency and engineering complexity, ensuring your model updates deliver real-world value. In this course, you will: Learn the foundational concepts of A/B testing and online experimentation in machine learning workflows; Design robust experiments for feed ranking, including core metric selection and sample size determination; Address unique feed challenges such as network spillover effects using modern cluster-based randomization techniques; Monitor key performance indicators, balancing user engagement gains with system latency and infrastructure costs; Analyze experimental results using statistical significance testing to make confident, data-driven launch decisions. The course begins with core terminology and experimental design principles before moving into the practical engineering challenges of testing machine learning models in production feeds. You will explore realistic scenarios and study step-by-step analyses of experiment outcomes. This course is designed for beginners, including aspiring data scientists, product managers, and software engineers who want to understand how modern recommendation systems are tested and optimized. No advanced statistics or prior machine learning experience is required. Start reading today to master the fundamentals of online experimentation for recommendation systems.