Featured Posts
Introducing Distributed XGBoost Training with Ray
XGBoost-Ray is a novel backend for distributed XGBoost training. It features mul...
Introducing Ray Lightning: Multi-node PyTorch Lightning training made easy
Ray Lightning is a new plugin that makes running multi-node GPU training with Py...
Analyzing memory management and performance in Dask-on-Ray
Ray is a general-purpose distributed system. One of Ray's goals is to seamlessl...
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How Ray and Anyscale make it easy to do massive-scale machine learning on aerial imagery
Dendra Systems’ mission is to enable faster, high-quality, transparent and scalable restoration. They do this using a variety of tools like drones for ultra-high resolution mapping at an unprecedented scale, machine learning algorithms that analyze i...
Ray version 1.7 has been released
Ray version 1.7 is here. Highlights include: Ray SGD v2 and is in alpha, Ray Workflows is in alpha, and major enhancements to the C++ API
Why Third Generation ML Platforms are More Performant
In a previous blog post, we defined a "3rd generation ML platform" as one that offered full programmability for ML workflows. Key to a 3rd generation platform is the concept of a programmable compute layer. In this blog, we report on emerging pattern...
Serving ML Models in Production: Common Patterns
Over the past couple years, we've listened to ML practitioners across many different industries to learn and improve the tooling around ML production use cases. Through this, we've seen 4 common patterns of machine learning in production: pipeline, e...
Wildlife Studios serves in-game offers 3X faster with Ray Serve
Mobile gaming giant Wildlife Studios’ legacy system for serving revenue-generating in-game offers was not scaling to meet their latency and cost requirements. After switching to Ray Serve on Anyscale, their Dynamic Offers team is now able to serve of...
The Third Generation of Production ML Architectures
As technology has advanced, production ML architectures have evolved. One way to see it is in terms of generations: The first generation involved “fixed function” pipelines, while the second generation involved programmability within the pipeline of...
Building Highly Available and Scalable Online Applications on Ray at Ant Group
Ant Group has developed Ant Ray Serving which is an online service framework based on Ray, which provides users with a Serverless platform to publish Java/Python code as online services. The platform provides users with basic capabilities such as dep...
Parallelizing Python Code
This article reviews some common options for parallelizing Python code including process-based parallelism, specialized libraries, ipython parallel, and Ray.
How Anastasia accelerated their ML processes 9x with Ray and Anyscale
Anastasia.ai provides a powerful platform that enables organizations to operate AI capacities at scale with a fraction of the resources and effort traditionally required. This post covers a demand prediction problem we had and how using Ray to solve...
An Introduction to Reinforcement Learning with OpenAI Gym, RLlib, and Google Colab
An introductory tutorial on reinforcement learning with OpenAI Gym, RLlib, and Google Colab.