Computer Science
5 years ago

Revolutionizing Machine Learning: Faster Results for End-Users with Distributed Architecture

Future Generation Computer Systems
Parallelizing Machine Learning as a service for the end-user
Daniela Loreti, Marco Lippi, Paolo Torroni

Paper Summary

The article discusses how to make machine learning services faster for users by using a distributed system. The researchers show how this method can be applied to text mining and similar tasks, and they found that using this system can greatly speed up the process of getting results.

Related papers

Theme background image
Engineering
8 years ago

Pipeline design breakthrough enhances safety and reliability for communities worldwide.

Seismic Design
Pipeline Corrosion
Water Distribution
Theme background image
Engineering
4 years ago

New pipeline scheme slashes data delays, revolutionizing microarchitecture efficiency.

Low-Power VLSI
Parallel Computing
Reconfigurable Computing
Theme background image
Computer Science
4 years ago

PowerShell Pipeline Revolutionizes Data Refinement Process in Windows Environment

Reconfigurable Computing
Power System Data
Embedded Systems