What you'll learn

In this course, Machine Learning Model Development, you’ll learn how to select, train, and deploy machine learning models with a focus on real-world performance constraints and cost-efficiency. Beginning with an overview of core model families—linear models, tree-based methods, and neural networks—you’ll explore how to weigh trade-offs like interpretability, resource requirements, and latency across use cases. You’ll then train and evaluate models using the UCI Adult dataset, analyze system resource usage with memory and CPU profiling tools, and assess implications for deployment on cloud infrastructure such as AWS. Next, you’ll package models for production with ONNX, deploy them using FastAPI, and test scalability through Docker containers and load testing frameworks like hey and Locust. Finally, you’ll examine advanced validation techniques—including stratified k-fold, blocked time splits, and progressive validation—and learn how to choose the right approach based on both upstream and downstream business constraints. By the end of the course, you’ll be equipped to make principled decisions about model selection, deployment architecture, and validation strategy to ensure robust, scalable, and cost-aware ML solutions.