ML Model Selection for System Design

Navigating the vast landscape of machine learning models to choose the optimal one for a specific system can be challenging. This course provides a foundational, structured framework to systematically evaluate and select machine learning models, ensuring they align with system requirements and constraints. You will learn to make informed decisions that balance accuracy, efficiency, and resource utilization. What you'll learn: * Understand fundamental machine learning model types and their inherent characteristics * Apply a structured framework for systematically evaluating potential model candidates * Analyze key model performance metrics and their trade-offs for different business objectives * Assess non-functional requirements such as latency, throughput, and computational cost * Consider data requirements, ethical implications, and fairness in model selection decisions * Explore basic considerations for model deployment and monitoring within an MLOps context The course begins by establishing core concepts of machine learning models and system design principles, then progresses through practical evaluation techniques and decision-making criteria. This course is designed for beginners interested in machine learning engineering, data science, or system design, with no prior experience in ML system design required. Start building your expertise in designing robust and efficient ML-powered systems today.