Deploying Machine Learning Applications on App Engine

Deploying machine learning models to production is an essential skill for modern developers and data practitioners. This text-based course guides you through packaging trained predictive models into reliable web APIs and hosting them on App Engine with minimal operational overhead. You will gain a solid understanding of how to bridge the gap between machine learning development and live cloud deployment, creating scalable endpoints ready to handle real-world user requests. What you'll learn: - Understand foundational cloud computing concepts, App Engine architecture, and deployment environments - Build lightweight web APIs with Python frameworks to serve machine learning predictions - Configure configuration files, environment variables, and dependencies for reliable App Engine deployment - Package model artifacts and manage storage efficiently for cloud-hosted applications - Apply basic containerization techniques and best practices for reproducible cloud runtime environments - Monitor application performance, manage scaling rules, and review execution logs effectively The course begins with foundational concepts and terminology before walking step by step through preparing model code, configuring service settings, and deploying production-ready prediction endpoints. This course is designed for beginners in cloud deployment, developers, and aspiring data professionals who want a clear, step-by-step introduction without prior DevOps experience. Start reading today to turn your machine learning scripts into accessible cloud services.