End-to-end Multimodal AI Workloads

While it may seem like this is a simple image semantic-search and classification demo — it’s really a deep dive into why Ray is the ideal platform for performant, reliable, and scalable multimodal AI workloads. Ray Data helps build a heterogeneous CPU/GPU pipeline that ingests images and runs large-scale batch inference to produce embeddings. Next, Ray Train offers distributed, fault-tolerant, multi-node, multi-GPU training—complete with experiment orchestration and model artifact management. Finally, Ray Serve deploys an embedding-based search service, achieving low-latency, autoscaling inference at production scale. Along the way, you’ll see how Ray’s unified resource management, autoscaling, and observability capabilities let you not only implement — but continuously monitor and optimize — every stage of your multimodal AI workflow.

• Image semantic search and classification
  • Development
  • Production
  • No infrastructure headaches
• Batch inference
  • Data ingestion
  • Batch embeddings
  • Production jobs
  • Similar images
• Distributed training
  • Preprocess
  • Model
  • Batching
  • Model registry
  • Training
  • Evaluation
• Online serving
  • Deployments
  • Application
  • Production services
  • CI/CD