Deploying Machine Learning Models with AWS S3 and Lambda

Deploying machine learning models shouldn't require complex infrastructure management. By leveraging serverless cloud storage and compute, you can get your models into production quickly, securely, and cost-effectively. This course guides you through the foundational concepts of cloud storage and serverless execution, showing you how to configure storage buckets, manage model artifacts, and set up automated deployment pipelines that scale automatically with demand. What you'll learn: - Understand core cloud storage concepts and AWS S3 bucket architecture - Configure secure access policies and IAM roles for model deployments - Upload and manage large machine learning model artifacts efficiently - Set up serverless Lambda functions to serve predictions from stored models - Implement Infrastructure as Code principles to automate resource creation - Monitor and optimize storage costs using modern lifecycle policies The course begins with essential cloud storage terminology and security fundamentals before moving into practical configuration of storage buckets. You will then progress to writing serverless functions that interact with your stored models, culminating in a complete, automated deployment workflow. Designed for beginner data scientists, machine learning enthusiasts, and software developers looking to transition their models to the cloud, this course requires no prior AWS experience. Start reading today to master the basics of serverless model deployment.