Deploying Custom ML Docker Images in AWS Lambda

Deploying machine learning models often runs into environment size limits and dependency issues in serverless environments. Packaging your models as custom Docker images for serverless deployment solves these challenges, allowing you to run robust inference at scale. This text-based course guides you through the process of building, testing, and deploying custom containerized machine learning models. You will transition from understanding basic serverless concepts to managing production-ready containerized deployments. What you'll learn: - Understand the core concepts of serverless computing, AWS Lambda limits, and containerization benefits - Build lightweight Docker images tailored for machine learning environments using multi-stage builds - Package popular machine learning libraries and models into compliant container structures - Test containerized Lambda functions locally before deploying them to the cloud - Configure AWS Elastic Container Registry to store and manage your custom images - Deploy Docker-based Lambda functions and optimize memory and timeout settings for inference - Apply basic MLOps best practices to monitor and update your deployed models Starting with foundational serverless and container terminology, this course walks you through configuring your environment, writing Dockerfiles for ML, and deploying to AWS. You will read step-by-step guides and analyze real-world configuration scripts. Designed for developers, data scientists, and engineers who are new to serverless container deployment, this course requires no advanced DevOps experience. Start reading today to master serverless machine learning deployments with custom containers.