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ANYSCALE
Production-scale AI with Ray
ABOUT
Distributed AI workloads — training large models, batch inference, and online serving at scale — require managing clusters, autoscaling, observability, and fault tolerance that most teams lack the operational expertise to handle. Anyscale eliminates this complexity by providing a fully managed platform built on Ray that scales from one GPU to thousands, with elastic autoscaling, GPU observability, and bring-your-own-cloud deployment so teams can focus on model development instead of cluster management.
INSTALL
pip install ray[default]INTEGRATION GUIDE
1. Run distributed training of large language models across hundreds of GPU workers with Ray Train
2. Execute batch inference and embedding generation at scale for production RAG pipelines
3. Serve models online with elastic autoscaling, GPU observability, and automatic failover
4. Process multimodal data curation and preprocessing pipelines at petabyte scale
5. Fine-tune LLMs with distributed optimization and deploy with Ray Serve for inference
TAGS
distributed-computingraygpu-clustermodel-trainingmlopsinfrastructureinferenceserving