Machine Learning Recommendation Engines on Cloud Platform

Modern digital platforms rely on recommendation engines to personalize user experiences, boost engagement, and drive revenue. Building these systems at scale requires a solid understanding of machine learning algorithms, data pipelines, and cloud infrastructure. This text-based course guides you through the process of designing, training, and deploying recommendation systems on Cloud Platform. You will transition from understanding foundational recommendation algorithms to implementing production-ready pipelines that deliver real-time, personalized suggestions. What you'll learn: - Understand the core concepts of collaborative filtering, content-based filtering, and hybrid recommendation systems. - Create and manage user and item embeddings to capture complex user-item interactions. - Build modern two-tower neural network models for candidate retrieval and ranking. - Implement machine learning workflows using BigQuery ML and Vertex AI. - Leverage modern vector search databases for low-latency, real-time recommendation retrieval. - Deploy, evaluate, and monitor recommendation pipelines to ensure reliable performance at scale. Starting with essential terminology and foundational algorithms, the written lessons and code snippets walk you through data preparation, model training, pipeline orchestration, and deployment strategies. This course is designed for developers, data analysts, and cloud engineers who want to specialize in recommendation workflows. A basic understanding of machine learning concepts and Python is recommended, but no prior cloud architecture experience is required. Start reading today to build smart recommendation engines that power modern user experiences.