Distributed libraries allow improved performance by exploiting the full bandwidth of distributed memory, and giving greater programmability. But how does that actually work? What does the code look like?

In this post, we’ll be looking at a concrete...

Why Jaikumar Ganesh joined Anyscale as Head of Cloud Engineering.

What is statistical forecasting and how you can use ARIMA and Prophet on Ray to speed up your forecasting.

Running and monitoring distributed ML systems can be challenging. The need to manage multiple servers, and the fact that those servers emit different logs, means that there can be a lot of overhead involved in scaling up a distributed ML system. Fort...

A common grumble among data science or machine learning researchers or practitioners is that putting a model in production is difficult. As a result, some claim that a large percentage, 87%, of models never see the light of the day in production.

“...

This blog showcases major enhancements that were made in Ray version 1.7 to the C++ API that make it much easier to build distributed C++ systems!

Learn what is new in the Ray Distributed Library Ecosystem as the Ray community of users, contributors, libraries, and production use cases have grown substantially since we first described the Ray ecosystem over a year ago.

This blog takes a closer look at digital identity verification, especially in the context of the cidaas ID validator and the modern Cloud Identity & Access Management solution – cidaas. You will get insight into what digital identity verification act...

Ray version 1.8 has been released! Release highlights include: Ray SGD has been renamed to Ray Train, Ray Datasets is now in beta, and experimental support for Ray on Apple Silicon (M1 Macs)

Ray is a framework with simple and universal APIs for building innovative AI applications. BigDL, an open-source framework for building scalable end-to-end AI on distributed Big Data, has leveraged Ray and its native libraries to support advanced AI...