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Try Managed Ray

Site Navigation

  • Get Started

  • Use Cases

  • Example Gallery

  • Library

    • Ray CoreScale general Python applications

    • Ray DataScale data ingest and preprocessing

    • Ray TrainScale machine learning training

    • Ray TuneScale hyperparameter tuning

    • Ray ServeScale model serving

    • Ray RLlibScale reinforcement learning

  • Docs

  • Resources

    • Discussion ForumGet your Ray questions answered

    • TrainingHands-on learning

    • BlogUpdates, best practices, user-stories

    • EventsWebinars, meetups, office hours

    • Success StoriesReal-world workload examples

    • EcosystemLibraries integrated with Ray

    • CommunityConnect with us

Try Managed Ray
  • Overview
  • Getting Started
  • Installation
  • Use Cases
    • Ray for ML Infrastructure
  • Examples
    • Multi-modal AI pipeline
      • Batch inference
      • Distributed training
      • Online serving
    • LLM training and inference
    • Audio batch inference
    • Distributed XGBoost pipeline
      • Distributed training of an XGBoost model
      • Model validation using offline batch inference
      • Scalable online XGBoost inference with Ray Serve
    • Time-series forecasting
      • Distributed training of a DLinear time-series model