The future of machine learning is distributed If you are familiar with ML model deployment, you may know about PMML and PFA. PMML and PFA are existing standards for packaging ML models for deployment.

Machine learning programming is an in-demand skill. Learn how to program an ML application with Python in this tutorial.

Training a machine learning algorithm to accurately solve complex problems requires large amounts of data. Previous articles in this series discussed an exascale-capable machine learning algorithm and ...

Uber AI has open-sourced Fiber, a new library which aims to empower users in implementing large-scale machine learning computation on computer clusters. The main objectives of the library are to ...

Over the last couple of decades, those looking for a cluster management platform faced no shortage of choices. However, large-scale clusters are being asked to operate in different ways, namely by ...

In this article we explore how privacy-preserving distributed machine learning from federated databases might assist governance in health care. The article first outlines the basic parameters of the ...

At the core of distributed big data architectures will be: 1) microservices and streaming; 2) distributed file system and DBMS; and 3) distributed machine learning.

TensorFlow 0.8 adds distributed computing support to speed up the learning process for Google's machine learning system.