Do you work in an AWS cloud environment and want to level up your skills by deploying a real machine learning model into production? Have you trained models before but felt unsure how to turn them into something users or applications can actually call?
If that sounds familiar, you’re in the right place.
Welcome to Amazon SageMaker Model Deployment with Lambda and API Gateway—a practical, hands-on course designed to help you connect the dots between machine learning and real-world AWS services.
In this course, you won’t just train a model and stop there. You’ll learn the complete deployment workflow, starting with building and training a machine learning model in Amazon SageMaker, deploying it to a managed SageMaker endpoint, and then exposing that model through a fully functional HTTP prediction API using AWS Lambda and Amazon API Gateway. By the end, you’ll understand how modern ML-powered applications are actually built in the cloud.
To keep things realistic, we’ll work with real data from the UCI Bank Marketing Database, and you’ll have access to the full data dictionary through the course downloads. Using this dataset, we’ll explore and answer a meaningful business question:
“Which bank customers are most likely to purchase a term deposit account?”
Every step of the process is demonstrated clearly, with all code provided in a downloadable JupyterLab notebook. You can follow along line by line, experiment freely, and reuse the project as a starter template for your own machine learning deployments.
Whether you’re a cloud engineer, data scientist, or ML practitioner looking to make your models production-ready, this course gives you practical, reusable skills you can apply immediately. No fluff—just clear explanations, real AWS services, and hands-on experience that will boost your confidence and your career.
Let’s turn your machine learning models into live, scalable APIs that actually deliver value!
