Designing Scalable Machine Learning Systems

Building a machine learning model is just one step; deploying and maintaining it at scale requires a different set of skills. This course provides you with the foundational understanding to design, develop, and evaluate machine learning systems that can handle increasing data volumes and user traffic, ensuring your models deliver consistent value. What you'll learn: * Understand the lifecycle of a machine learning system, from problem definition to maintenance. * Design system architectures for data ingestion, model training, inference, and monitoring. * Apply strategies for ensuring data quality, feature engineering, and model versioning. * Evaluate model performance and system health using appropriate metrics and feedback loops. * Implement basic MLOps principles for continuous integration and deployment of ML models. * Explore fundamental concepts of model serving and API design for ML applications. The course begins with core concepts and problem framing, then progresses through architectural design patterns, data handling, model lifecycle management, and system evaluation. This course is for beginners in machine learning or software development who want to understand how to build and operate ML solutions in a production environment, with no prior experience in system design required. Start your journey towards becoming proficient in scalable machine learning system design.