Deploying Machine Learning Models on AWS with Serverless

You've trained a machine learning model, but how do you get it into production without the complexity of managing servers? This course provides a practical path to deploying your models as live, scalable web services using a serverless approach. By the end of this course, you'll be able to confidently package various ML models, define the necessary cloud infrastructure as code, and deploy them as robust APIs on AWS. You will move from a trained model file to a fully functional, production-ready endpoint that can serve predictions on demand. What you'll learn: - Understand core serverless concepts and key AWS services like Lambda, API Gateway, and S3. - Configure and manage cloud resources declaratively using the Serverless Framework. - Package scikit-learn, Keras, and other ML models with their dependencies for serverless environments. - Build and deploy ML services using both traditional ZIP packages and modern container images for AWS Lambda. - Create secure and scalable HTTP APIs to serve real-time predictions from your models. - Practice writing Python handler functions to load models and process inference requests efficiently. - Implement basic logging and monitoring for your serverless applications using CloudWatch. The course begins with foundational serverless principles and an introduction to the core AWS services you'll use. From there, you'll progress through hands-on written exercises, deploying increasingly complex machine learning models. This course is designed for beginners in cloud deployment. While basic familiarity with Python and machine learning concepts is helpful, no prior experience with AWS or the Serverless Framework is required. Start learning how to take your models from your local machine to production today.