Deploying Machine Learning Models with AWS Lambda and Docker

Deploying machine learning models to production often requires managing complex infrastructure, leading to high idle costs and scaling headaches. Serverless architectures combined with containerization offer a powerful, cost-effective way to run predictions on demand without managing servers. In this text-based course, you will learn how to package machine learning models into lightweight Docker containers and deploy them to AWS Lambda. You will transition from writing local Python inference code to launching highly scalable, production-ready serverless endpoints. What you'll learn: - Understand the foundational concepts of serverless computing and containerized deployments for machine learning. - Build Docker images tailored specifically for running ML inference inside AWS Lambda. - Configure AWS Lambda functions to serve predictions using container images. - Optimize container size and model loading to minimize serverless cold starts. - Implement basic monitoring and logging using CloudWatch to track model performance. - Apply infrastructure-as-code principles to manage and update your deployments reliably. The course begins with core definitions and the theory of serverless execution before guiding you through step-by-step configuration, container creation, and live AWS deployment workflows. You will practice through structured written exercises and real-world deployment scenarios. This course is designed for beginner data scientists, software developers, and aspiring ML engineers who want to learn deployment basics. No prior cloud engineering experience is required, though basic Python knowledge is recommended. Start reading today to master the essentials of modern serverless model deployment.