RAY
Anyscale Launches Agent Skills to Streamline AI on Ray
Anyscale's new Agent Skills enhance AI coding tools like Claude Code and Cursor, optimizing Ray-based workflows for speed and scalability.
Notion Slashes AI Embedding Costs 80% After Ditching Spark for Ray
Notion migrated from Spark on EMR to Ray, cutting embedding costs 80% and improving query latency 10x. Uber and Salesforce shared similar AI infrastructure wins.
Ray 2.55 Adds Fault Tolerance for Large-Scale AI Model Deployments
Anyscale's Ray Serve LLM update enables DP group fault tolerance for vLLM WideEP deployments, reducing downtime risk for distributed AI inference systems.
VLA Models Reshape Robotics as $94B Market Embraces AI Infrastructure
Vision-Language-Action models are driving robotics teams to Ray and Anyscale for distributed training. Market projected to hit $94.38B by 2031.
Ray's Disaggregated Hybrid Parallelism Boosts Multimodal AI Training by 30%
Ray's innovative disaggregated hybrid parallelism significantly enhances multimodal AI training efficiency, achieving up to 1.37x throughput improvement and overcoming memory challenges.
Anyscale Showcases AI Innovations at AWS re:Invent 2025
Anyscale highlights AI solutions with Ray at AWS re:Invent 2025, featuring demos, talks, and executive events for enhanced machine learning operations.
Ray Enhances Scheduling with New Label Selectors
Ray introduces label selectors, enhancing scheduling capabilities for developers, allowing more precise workload placement on nodes. The feature is a collaboration with Google Kubernetes Engine.
Enhancing Ray Clusters with NVIDIA KAI Scheduler for Optimized Workload Management
NVIDIA's KAI Scheduler integrates with KubeRay, enabling advanced scheduling features for Ray clusters, optimizing resource allocation and workload prioritization.
Enhancing Text-to-SQL Models Using Tinker and Ray
Discover how Tinker and Ray are utilized to fine-tune text-to-SQL models, enhancing AI capabilities in generating efficient SQL queries.
Tencent's Weixin Integrates Ray for Large-Scale AI Deployment
Tencent's Weixin team has embraced Ray and Kubernetes to enhance their AI infrastructure, tackling challenges in resource utilization and deployment complexity.
Ray and the Evolution of AI Compute Frameworks
Explore how Ray addresses compute bottlenecks in AI frameworks, as unstructured data and GPU demands challenge legacy systems, according to Anyscale.
Exploring the Open Source AI Compute Tech Stack: Kubernetes, Ray, PyTorch, and vLLM
Discover the components of a modern open-source AI compute tech stack, including Kubernetes, Ray, PyTorch, and vLLM, as utilized by leading companies like Pinterest, Uber, and Roblox.
Enhancing RAG Pipelines with Ray and Anyscale for Scalable AI Solutions
Explore how Ray and Anyscale empower developers to build scalable Retrieval-Augmented Generation (RAG) pipelines, reducing hallucinations and integrating new information without retraining models.
Anyscale Introduces Comprehensive Ray Training Programs
Anyscale launches new training options for Ray, including free eLearning and instructor-led courses, catering to AI/ML engineers seeking to scale AI applications effectively.
Efficient Python Dependency Management in Clusters with uv and Ray
Explore how the integration of uv and Ray enhances Python dependency management in distributed systems, facilitating efficient environment setups and consistent execution across clusters.