Rise of hybrid machine learning computing – By Aditya Abeysinghe Distributing models Distributed machine learning is used to decentralize computation in machine learning models to individual nodes rather than computing in a centralized model. Distributed machine learning removes issues with large processing queues where devices have to send data to centralized computational models and obtain responses. Distributing models is always not viable as most nodes have limited storage resources and computational resources. Using models that are near to a node Centralized computing of models reduces issues with computation limits and storage limits. Models that are used by large volume of users often use centralized method of processing. However, the time to receive an output is high due to time for computing in the server and time taken to communicate data between the user and the server. In contrast, distributed models are faster as they need no time to communicate. However, ...
Dec 10, 2022
Posted
Aditya Abeysinghe, Articles
Tagged Centralized computing, data handling, Distributed machine learning, Distributing models, federated model, machine learning models
