Machine Learning Model Deployment with FastAPI, Docker, and AWS

Many machine learning models never leave the development environment because the path to production feels complex and fragmented. This course bridges that gap by teaching you how to take state-of-the-art Transformer models and turn them into robust, accessible web services. You will move beyond local scripts to build professional-grade applications that are ready for the real world. By the end of this course, you will have a clear understanding of the entire deployment lifecycle, from selecting pre-trained models to hosting them on scalable cloud platforms. You will gain the skills to build efficient APIs, create interactive interfaces, and manage infrastructure using industry-standard tools. * Understand foundational concepts of Transformer architectures like BERT and ViT * Build high-performance web APIs for model inference using FastAPI * Create interactive user interfaces for machine learning tools with Streamlit * Containerize applications using Docker for consistent execution across environments * Configure cloud infrastructure on AWS to host and scale your deployments * Apply model optimization and quantization techniques to improve inference speed * Implement basic MLOps workflows for model versioning and lifecycle management The course begins with essential terminology and the basics of model serving before moving into practical API development and containerization. You will then explore how to navigate cloud environments to ensure your models are secure, reliable, and performant. This course is designed for beginners in data science and software engineering who want to move into the world of MLOps; no prior deployment experience is required. Start building and deploying your own production-ready machine learning applications today.