Machine learning (ML) architecture refers to the layout and design principles for developing machine learning models. These rules include the development, deployment, and administration of models using machine learning. It comprises software and hardware components, including algorithms, data pipelines, and computational infrastructure necessary for training and model serving. Machine learning architecture is fundamentally designed to support all stages of workflow such as data analysis, preprocessing, model training, validation, and inference, guaranteeing a scalable, maintainable, and robust system.