Deploying Machine Learning Models on AWS, Azure, and GCP

Building a machine learning model is only half the battle; the real value comes when you deploy it to the cloud so others can use it. Navigating the deployment tools of the three major cloud providers can feel overwhelming for beginners. This text-based course guides you through the process of taking your machine learning models from a local environment and deploying them successfully to AWS, Azure, and GCP. You will understand how to choose the right services, configure hosting environments, and apply modern MLOps principles to keep your models running smoothly in production. What you'll learn: Understand foundational cloud computing concepts and key terminology for machine learning deployment; Configure and launch machine learning models on AWS using SageMaker; Deploy scalable model endpoints on Azure Machine Learning services; Utilize GCP Vertex AI to host and manage predictive models; Package your models into containers using Docker for consistent, environment-agnostic deployment; Apply basic MLOps practices, including simple CI/CD pipelines and model monitoring. The course begins with essential definitions of cloud infrastructure and model hosting. You will then progress through step-by-step written guides for each major cloud platform, followed by practical exercises on containerization and basic model monitoring. Designed specifically for beginners, this course requires no prior cloud experience, though a basic understanding of Python and machine learning concepts is helpful. Start reading today to bridge the gap between model training and cloud deployment.