The Emergence of Multi-cloud Native Applications and Platforms

As companies go multi-cloud, a new set of tools simplify IT management and application development. By Ben Lorica and Ion Stoica. [This post originally appeared on the Anyscale blog.] In a previous post we examined current serverless computing offerings and described why Ray is an ideal substrate for general purpose computing platforms. While serverless hasContinue reading “The Emergence of Multi-cloud Native Applications and Platforms”

Towards an infinite laptop

The new Anyscale platform offers the ease of development on a laptop combined with the power of the cloud. During a series of short keynotes at the Ray Summit this morning, Anyscale1, the company formed by the creators of Ray, publicly shared their initial product offering. Dubbed the “infinite laptop”,  Anyscale’s platform allows developers toContinue reading “Towards an infinite laptop”

Five Key Features for a Machine Learning Platform

ML platform designers need to meet current challenges and plan for future workloads. By Ben Lorica and Ion Stoica. [This post originally appeared on the Anyscale blog.] As machine learning gains a foothold in more and more companies, teams are struggling with the intricacies of managing the machine learning lifecycle. The typical starting point isContinue reading “Five Key Features for a Machine Learning Platform”

Understanding the Ray ecosystem and community

Ray is both a general purpose distributed computing platform and a collection of libraries targeted at machine learning and other workloads. By Ben Lorica and Ion Stoica. [This post originally appeared on the Anyscale blog.] Ray is usually described as a distributed computing platform that can be used to scale Python applications with minimal effort.Continue reading “Understanding the Ray ecosystem and community”

Introducing RLlib: A composable and scalable reinforcement learning library

[A version of this post appears on the O’Reilly Radar.] RISE Lab’s Ray platform adds libraries for reinforcement learning and hyperparameter tuning. In a previous post, I outlined emerging applications of reinforcement learning (RL) in industry. I began by listing a few challenges facing anyone wanting to apply RL, including the need for large amountsContinue reading “Introducing RLlib: A composable and scalable reinforcement learning library”