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Why I joined Anyscale
I’m honored to join the world-class team at Anyscale for two reasons: I believe in the purpose-driven mission and I’m blown away by the team. Joining Anyscale felt like joining a movement to democratize machine learning; we’re providing equal access...
Cheaper and 3X Faster Parallel Model Inference with Ray Serve
Wildlife Studios’s ML team was deploying sets of ensemble models using Flask. It quickly became too hard and too expensive to scale. By using Ray Serve, Wildlife Studios was able to improve the latency and throughput while reducing the cost. Ray Serv...
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.