Ray

Big Data

Ray Summit 2023: Day 1 Keynote

Ray is the most popular open source framework for scaling and productionizing AI workloads. From Generative AI and LLMs to computer vision, Ray powers the world’s most ambitious AI workloads.

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AI Generative AI Red Hat

Unlock the Power of Generative AI with the Right Platform Stack

Taneem Ibrahim and Selbi Nuyryev describe the work in the Open Data Hub community about building an AI/ML stack for generative AI and foundation models. With project CodeFlare and open source technology work with CAIkit, KubeRay and Text Generation Inference Service (TGiS), KServe to prompt-tune pre-trained models and provide resource efficiency across the hybrid cloud. […]

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Big Data Spark

Scaling and Unifying SciKit Learn and Spark Pipelines using Ray

Pipelines have become ubiquitous, as the need for stringing multiple functions to compose applications has gained adoption and popularity. Common pipeline abstractions such as “fit” and “transform” are even shared across divergent platforms such as Python Scikit-Learn and Apache Spark. Scaling pipelines at the level of simple functions is desirable for many AI applications, however […]

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AI Open Source Python Red Hat

Accelerating AI with Python-native Ray and the Importance of Open Source in AI

On this episode of Data Driven, I explore the topic of distributed computing frameworks for AI and ML workloads. I also discuss the advancements of Ray, a new technology based on Python language, with performance enhancements that could range from 10-12 times faster to thousands of times faster in extreme cases. We delve into the […]

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AI

Ray A Framework for Scaling and Distributing Python & ML Applications | Anyscale

Modern machine learning (ML) workloads, such as deep learning and large-scale model training, are compute-intensive and require distributed execution. Ray is an open-source, distributed framework from U.C. Berkeley’s RISELab that easily scales Python applications and ML workloads from a laptop to a cluster, with an emphasis on the unique performance challenges of ML/AI systems. It […]

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