Ray Data is a distributed Dataset engine for loading, transforming, and feeding massive datasets into ML models and LLMs—at scale, with fault tolerance.
Ray Data integration would allow Datatune to:
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Process datasets that exceed single-node or cluster memory
-
Automatically handle sharding, load balancing, and fault tolerance
-
Scale batch LLM inference across multi-GPU and multi-node clusters without code changes
-
Support both offline and online inference workflows
-
Strengthen on-premise and self-hosted deployments using Ray or Kubernetes
Ray Data is a distributed Dataset engine for loading, transforming, and feeding massive datasets into ML models and LLMs—at scale, with fault tolerance.
Ray Data integration would allow Datatune to:
Process datasets that exceed single-node or cluster memory
Automatically handle sharding, load balancing, and fault tolerance
Scale batch LLM inference across multi-GPU and multi-node clusters without code changes
Support both offline and online inference workflows
Strengthen on-premise and self-hosted deployments using Ray or Kubernetes