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Richard Liaw

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The Ray 2.3 release features exciting improvements across the Ray ecosystem. In this blog post, we will highlight new features, performance enhancements, and support for new platforms. 

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Announcing Ray 2.3: performance improvements, new features and new platforms

Avnish Narayan

Christy Bergman

Jun Gong

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Reinforcement learning sessions at Ray Summit: A guided tour

Ben Lorica

7 must-attend Ray Summit sessions: RL-powered traffic control, infra-less ML, and more

Erik Martinez

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Riot Games and deep reinforcement learning in gaming

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Reinforcement learning based on market simulation at JPMorgan

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Smart supply chain management with reinforcement learning at Dow

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Siemens brings reinforcement learning to energy, transportation and logistics

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Biggest takeaways from our RL tutorial: Long-term rewards, offline RL, and more

Chandler Gibbons

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5 reasons to attend this month’s Production RL Summit

Misha Laskin

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Practical tips for training Deep Q Networks

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Reinforcement learning with Deep Q Networks

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Sailing to victory with reinforcement learning 

This is part 2 of our generative AI blog series. Here, we cover how Ray empowers large language models (LLM) frameworks such as Alpa. 

Figure 5: Technical integration layered stack for LLM


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High performance LLM training at 1000 GPU scale with Alpa and Ray

Training 175B Parameter Language Models at 1000 GPU scale with Alpa and Ray

This is part 1 of our generative AI blog series. In this blog, we talk about how to use Ray to deploy common generative model workloads in production. An upcoming blog will deep dive into why projects like Alpa are using Ray to scale large models.

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Ray solves Generative AI and LLM Infrastructure Challenges

How Ray solves common production challenges for generative AI infrastructure

Exoshuffle is now the most cost-efficient way to sort 100TB of data on the public cloud, using only $0.97 per terabyte. Learn how this is possible with Ray.

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Ray Is the World’s Most Cost-Efficient Sorting System at $1/TB

Ray breaks the $1/TB barrier as the world’s most cost-efficient sorting system

More companies are needing to train & deploy many small ML models, often hundreds or thousands. Learn how Ray and Anyscale make that possible.

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Training 1 Million ML Models in Record Time | Anyscale

Training One Million Machine Learning Models in Record Time with Ray

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From the creators of Ray, Anyscale is a framework for building machine learning applications at any scale originating from the UC Berkeley RISELab.

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Scalable AI and Python

Featured Posts and News

Training 175B Parameter Language Models at 1000 GPU scale with Alpa and Ray

How Ray solves common production challenges for generative AI infrastructure

Ray breaks the $1/TB barrier as the world’s most cost-efficient sorting system

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