Centralized Machine Learning
Centralized Machine Learning is a traditional approach where all training data is aggregated into a single location, typically a central server or data center, to train machine learning models. This method involves collecting data from various sources, processing it centrally, and using it to build and update models in a unified environment. It contrasts with distributed approaches like federated learning, where data remains decentralized.
Developers should use centralized machine learning when they have access to a consolidated dataset, require high model accuracy with full data visibility, and operate in environments with minimal privacy or bandwidth constraints. It is ideal for applications like image recognition on cloud servers, recommendation systems with centralized user data, and scenarios where data can be legally and efficiently aggregated, such as in enterprise analytics or research projects.