Deploying AI Inference Models on Cloud Run

Deploying machine learning models to production can be complex, but serverless containers simplify the entire process. This text-based course guides you through the foundational concepts of AI inference and how to host your models efficiently without managing servers. By completing this course, you will transition from having a locally trained model to running a fully scalable, secure, and cost-effective inference API on Cloud Run. What you'll learn: - Understand the core concepts of AI inference, prediction latency, and serverless architecture. - Containerize machine learning models using Docker with optimized, lightweight base images. - Deploy your containerized models to Cloud Run for instant, auto-scaling web endpoints. - Configure environment variables, memory allocation, and CPU settings to optimize inference performance. - Manage cold starts and scale-to-zero behavior to balance cost and responsiveness. - Monitor deployment health and inference latency using modern observability practices. The course starts with essential terminology and foundational setup, then moves step-by-step through containerization, deployment configurations, and production scaling strategies. You will learn by reading clear explanations and studying practical configuration templates. This course is designed for beginner developers, data scientists, and cloud enthusiasts looking to deploy their first AI model. No prior cloud deployment experience is required. Start reading today to bring your machine learning models to the cloud with confidence.