Deploying and Scaling AI Models with Cloud Run

Bringing machine learning models from a local environment to a reliable production system can feel like an overwhelming challenge. This course demystifies the process by showing you how to leverage serverless container technology to serve AI models efficiently and cost-effectively. You will transition from working with offline models to deploying live, auto-scaling inference APIs. By understanding the fundamentals of containerization and serverless architecture, you will be able to handle real-world traffic demands without managing complex underlying infrastructure. What you'll learn: Understand foundational AI inference concepts and serverless architecture basics; Package machine learning models into lightweight, production-ready containers; Deploy containerized AI models to Cloud Run for instant web-accessible APIs; Configure automatic scaling and resource allocation to manage varying traffic loads; Optimize container startup times and memory usage for fast model response times; Implement basic monitoring and logging to track model performance in production. We begin with core definitions of machine learning inference and containerization, establishing a solid foundation before moving into practical configuration. You will then progress through step-by-step written explanations on packaging models, deploying them to Cloud Run, and fine-tuning scaling parameters. This course is designed for aspiring data scientists, software developers, and cloud beginners who want to learn model deployment without prior DevOps experience. Start reading today to master the essentials of modern serverless AI deployment.