Compute-intensive applications that incorporate machine learning should be built on top of Ray. By Ben Lorica and Ion Stoica. [This post originally appeared on the Anyscale blog.] Introduction As machine learning and AI become prevalent in software services and applications, most backend platforms now consist of business logic and machine learning (inference). Business logic andContinue reading “Why you should build your AI Applications with Ray”
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”
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”
Ray Serve simplifies the integration of classic web serving and model serving. By Ben Lorica and Ion Stoica.
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”
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”
[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”