Deploy and Scale AI Models with Cloud Run

Getting a machine learning model to work on your local machine is only the first step; the real challenge lies in serving it to users reliably and cost-effectively. Cloud Run offers a powerful, serverless environment to deploy and scale your AI models without the complexity of managing infrastructure. This text-based course guides you through the entire lifecycle of model deployment, from foundational containerization concepts to scaling configurations. You will gain the practical skills needed to turn your trained models into production-ready web APIs. What you'll learn: - Understand foundational cloud deployment concepts and serverless architecture basics. - Containerize machine learning models using Docker for consistent, reproducible environments. - Deploy AI models to Cloud Run to handle incoming prediction requests via web APIs. - Configure automatic scaling policies to optimize performance and minimize hosting costs. - Implement basic observability and logging to monitor model performance in real time. - Practice secure deployment workflows using modern infrastructure-as-code principles. The course starts with essential terminology and the basics of model inference before moving step-by-step through container packaging, deployment commands, and scaling strategies. Through clear text explanations and practical code examples, you will build a solid deployment pipeline. This program is designed for beginner developers, aspiring data scientists, and cloud enthusiasts, with no prior cloud deployment experience required. Start reading today to take your machine learning models from your local environment to the cloud.