Deploying and Scaling AI Models with Cloud Run

Getting a machine learning model to work on your local machine is only the first step; the real value comes when you deploy it so others can use it. This text-only course guides you through the process of taking trained AI models and turning them into scalable, production-ready web services using Cloud Run. You will start with the absolute fundamentals of AI inference and containerization, establishing a solid conceptual foundation before moving into practical deployment strategies. By reading through clear explanations and structured code examples, you will learn how to package your models, configure serverless environments, and manage traffic efficiently without the overhead of managing complex infrastructure. What you'll learn: - Understand the core concepts of AI inference and container-based deployment - Containerize machine learning models using lightweight Docker configurations - Deploy models to Cloud Run to create secure, accessible API endpoints - Configure auto-scaling, concurrency, and cold-start optimization for AI workloads - Implement basic observability and logging to monitor model performance in production - Apply modern MLOps practices to update and version your deployed models. The course begins with essential definitions and container basics, then progresses systematically through writing API wrappers, building container images, and deploying to the cloud. Designed specifically for beginners, this course requires no prior cloud deployment experience, though a basic familiarity with Python is helpful. Start reading today to transform your local machine learning models into highly scalable cloud APIs.