Serverless Model Deployment on AWS and GCP

Deploying machine learning models shouldn't require complex infrastructure management. By leveraging serverless managed services, you can get your models into production quickly, securely, and cost-effectively. This text-based course guides you through the core concepts and practical steps of serverless model deployment. You will transition from understanding basic model serialization to deploying production-ready APIs on AWS Lambda and GCP Cloud Run, complete with modern MLOps best practices. What you'll learn: Understand the foundational architecture of serverless model hosting; Configure AWS Lambda and GCP Cloud Run for machine learning inference; Deploy containerized models to overcome serverless storage limitations; Manage cold starts and optimize performance for real-time predictions; Integrate API Gateways to expose your models securely to the web; Apply modern MLOps concepts to monitor and update your deployed models. The course begins with essential definitions and architectural concepts before guiding you through step-by-step deployment scenarios on both major cloud platforms. You will learn through clear explanations, conceptual breakdowns, and written configuration walkthroughs. Designed for beginner data scientists, software engineers, and cloud enthusiasts looking to deploy their first models without deep DevOps experience, this course requires no prior cloud infrastructure knowledge. Start reading today to bridge the gap between machine learning development and cloud deployment.