Ray Data
Ray Data Adds GPU-Native Operators With NVIDIA cuDF Integration
Anyscale's Ray Data now supports NVIDIA cuDF and RapidsMPF, delivering up to 3x better TCO for GPU-accelerated AI data processing workloads.
Ray Data Shuffle V2 Boosts Performance, Scalability
Ray Data's Shuffle V2 improves distributed processing with up to 53x faster joins and groupbys, addressing memory and recovery challenges.
Ray Data 2.56 Enhances AI Pipelines with Zero OOM Errors
Ray Data 2.56 eliminates OOM errors, reduces memory pressure, and improves AI training/inference speeds by over 50%.
Scaling Multimodal Data Pipelines with Ray Data
Ray Data pioneers scalable multimodal data pipelines, optimizing GPU utilization and cutting costs for AI workloads.
Ray Data and Docling Tackle Enterprise AI's Biggest Pain Point
New integration combines Ray Data's distributed processing with Docling's document parsing to process 10k+ complex files for RAG applications in hours instead of days.
Anyscale Enhances Ray Data with Joins and Hash-Shuffle for Improved Performance
Anyscale introduces a hash-based shuffle backend in Ray Data, enhancing joins and performance for repartitioning and aggregations. Discover the advancements in the Ray 2.46 release.
Anyscale Launches Ray Train and Ray Data Dashboards for Enhanced Observability
Anyscale introduces Ray Train and Ray Data Dashboards, offering new features for improved observability and performance optimization in distributed AI model training and data pipelines.