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”