anyscale-guidelisted

## Overview Anyscale is the managed platform for Ray, the distributed computing framework — it runs your Ray workloads (training, batch processing, hyperparameter tuning, model serving with Ray Serve) on managed clusters without you operating the infrastructure. For AI teams, the practical shape is: develop Ray code locally, then run it at scale on Anyscale with managed cluster lifecycle, observability, and cost controls. The distinctive value is Ray itself: a unified programming model for distributed Python that covers training, serving, and data processing in one framework. If your ML work spans "train a model," "tune hyperparameters," "run batch inference," and "serve the model," Ray does all four — and Anyscale removes the cluster-management burden. It's infrastructure for teams whose workloads are genuinely distributed, not just "a GPU in the cloud." Anyscale's lens: if you're reaching for Ray, use the managed platform; if you're not using Ray, Anyscale probably isn't your starting point. ## When to use - Distributed training jobs that need multi-node/multi-GPU orchestration. - Hyperparameter sweeps at scale (Ray Tune) without managing the cluster. - Model serving with Ray Serve: composable deployments, multi-model pipelines. - Batch inference over large datasets with distributed processing. - Reinforcement learning workloads (Ray RLlib) at scale. - Teams standardizing on Ray as their distributed compute layer. ## Core concepts - **Ray core primitives**: tasks (st