Examples

• End-to-end Multimodal AI Workloads
  • Image semantic search and classification
  • Batch inference
  • Distributed training
  • Online serving
• Entity Recognition with LLMs
  • Set up
  • Data ingestion
  • Distributed fine-tuning
  • Batch inference
  • Online serving
  • Production
• Audio dataset curation using LLM-as-a-judge
  • Audio dataset curation with offline batch inference
• End-to-end distributed XGBoost
  • Overview
  • Dependencies
  • Distributed training of an XGBoost model
  • Model validation using offline batch inference
  • Scalable online XGBoost inference with Ray Serve
• End-to-end time-series
  • Time-series forecasting with DLinear
  • Distributed training of a DLinear time-series model
  • Anatomy of a Ray Train job
  • DLinear model validation using offline batch inference
  • Data ingest
  • Online serving for DLinear model using Ray Serve
• Object detection
  • Face Mask Detection Pipeline
  • Fine-tuning a face mask detection model with Faster R-CNN
  • Object detection batch inference on test dataset and metrics calculation
  • Video processing with object detection using batch inference
  • Host an object detection model as a service
• End-to-end RAG Deep Dive
  • End-to-end RAG Deep Dive
  • Build a Regular RAG Document Ingestion Pipeline (No Ray required)
  • Scalable RAG Data Ingestion and Pagination with Ray Data
  • Deploy LLM with Ray Serve LLM
  • Build Basic RAG App
  • Improve RAG with Prompt Engineering
  • Evaluate RAG with Online Inference
  • Evaluate RAG using Batch Inference with Ray Data LLM