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anyscale-guide
Scale Ray workloads and serve models on Anyscale — managed Ray clusters for training and inference.
skills/anyscale-guide/SKILL.md
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## 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