Deploying and Scaling AI Models with Cloud Run

Bringing machine learning models from a local environment to a reliable production system can be a daunting challenge. This text-based course guides you through the process of packaging, deploying, and scaling your AI models efficiently using Cloud Run. You will transition from training models locally to launching robust, auto-scaling inference endpoints in the cloud. By mastering serverless deployment, you will ensure your AI applications can handle fluctuating traffic without manual infrastructure management. What you'll learn: - Understand foundational AI inference concepts and serverless container architecture - Containerize machine learning models using Docker for seamless cloud deployment - Configure Cloud Run services to optimize memory, CPU, and scaling behaviors for AI workloads - Implement secure API endpoints to serve model predictions to web applications - Apply modern MLOps practices, including basic continuous integration and versioning for your model artifacts - Monitor and troubleshoot deployed models using cloud logging and performance metrics. The course begins with key terminology, basic concepts, and foundational definitions of containerization and serverless computing. You will then progress through step-by-step written guides and configuration exercises to package, deploy, and scale your own model endpoints. This course is designed for beginner developers, data scientists, and aspiring cloud engineers looking to deploy their first AI models. No prior cloud infrastructure experience is required. Start reading today to turn your offline machine learning models into scalable, production-ready cloud APIs.