Examples

• Multi-modal AI pipeline
  • Get the code
  • Development
  • Production
  • No infrastructure headaches
• LLM training and inference
  • Get the code
  • Set up
  • Data ingestion
  • Fine-tuning
  • Batch inference
  • Online serving
  • Production
• Audio batch inference
  • Setup
  • Get the code
  • Streaming data ingestion
  • Audio preprocessing
  • GPU inference with Whisper
  • LLM-based quality filter
  • Persist the curated subset
• Distributed XGBoost pipeline
  • Get the code
• Time-series forecasting
  • Get the code
  • Setup
  • Acknowledgements
• Scalable video processing
  • Get the code
• Distributed RAG pipeline
  • Get the code
  • Notebooks
• Deploy MCP servers
  • Why Ray Serve for MCP
  • Anyscale service benefits
  • Prerequisites
  • Development
  • Production
  • No infrastructure headaches
  • Get the code
• Build a tool-using agent
  • Get the code
  • Architecture overview
  • Dependencies and compute resource requirements
  • Implementation: Building the services
  • Deploy the services
  • Test the agent
  • Next steps
• Build a multi-agent system with the A2A protocol
  • 1. Architecture
  • 2. Project structure
  • 3. Get started with local deployment
  • Get the code
  • 4. Deploy to production on Anyscale
  • 5. Deep dive: Understand each component
  • 6. Next steps
  • 7. Additional resources