Deploying AI Inference Models with Cloud Run

Deploying trained machine learning models into production can be challenging, especially when managing infrastructure and scaling. This text-based course guides you through using Cloud Run to host your models as highly scalable, cost-efficient serverless API endpoints. By studying our structured written explanations and step-by-step configuration examples, you will transform raw model files into reliable cloud services. You will understand how to containerize your code, optimize memory settings, and manage traffic with ease. What you'll learn: - Understand the core concepts of serverless AI inference and containerization. - Package machine learning models using Docker for cloud deployment. - Configure Cloud Run services to optimize memory, CPU, and scaling parameters. - Create secure API endpoints to serve model predictions to web applications. - Apply modern practices for handling cold starts and monitoring model performance. - Implement basic security controls to protect your deployed inference endpoints. The course begins with essential terminology and the fundamentals of containerized web services, progressing through the practical steps of deploying and testing your first inference pipeline. This course is designed for beginners, requiring only basic programming concepts and no prior cloud infrastructure experience. Start reading today to bring your machine learning models to the cloud.