Introduction to Model Deployment with Ray Serve
Learn to deploy ML models using Ray Serve. Explore Ray Core APIs, create scalable architectures, and integrate with MLflow and FastAPI. Gain hands-on experience in distributed computing and model deployment.
ByteDance's Platform for Reinforcement Learning from Human Feedback
Explore ByteDance's journey in building a video data processing pipeline using Ray's ecosystem for creating realistic video generation models from text instructions.
Klaviyo's Journey to Robust Model Serving with Ray Serve
Explore Klaviyo's journey in building DART, a model serving platform using Ray Serve. Gain insights into architecture, deployment strategies, and practical advice for implementing Ray Serve in your own projects.
Faster Model Serving with Ray and Anyscale - Ray Summit 2024
Explore Ray Serve's capabilities for distributed model serving and deployment, focusing on Anyscale's solutions for large-scale AI models and the challenges of building AI applications in the era of generative AI.
Scaling Inference Deployments with NVIDIA Triton Inference Server and Ray Serve
Explore the integration of Ray Serve and NVIDIA Triton Inference Server for enhanced ML model performance and scalability. Learn to optimize inference deployments using advanced tools and a new Python API.
Building Intelligent AI Infrastructure with ORI - Dynamic Query Routing and Model Management
Discover how ORI dynamically routes AI queries to optimal language models, featuring chain atomic queries, benchmark matching, and ethical frameworks integrated with Ray Serve and vLLM for high-speed inference.
Scaling LLM Inference - AWS Inferentia Meets Ray Serve on EKS
Discover how to build scalable, cost-effective LLM inference infrastructure using Ray Serve and AWS Inferentia on Amazon EKS, optimizing deployment of models like Llama2 and Mistral-7B.
Building Scalable Cross-Modal Search with Ray
Discover how to build a powerful fashion image retrieval system using Ray and Pinecone, enabling efficient text-to-image and image-to-image search with CLIP embeddings and vector similarity.
High-Performance AI Model Serving with Ray Serve - A Rubrik Case Study
Discover how Rubrik achieved high-performance AI inference using Ray Serve, exploring scalability solutions, fault tolerance strategies, and deployment techniques for handling millions of daily model evaluations.
Distributed Mesh Processing and Geometric Deep Learning for Wind Prediction
Discover how Ray's distributed computing powers real-time wind prediction in urban design through mesh processing, geometric deep learning, and CFD data analysis for architectural innovation.
Patterns of ML Models in Production
Explore ML model deployment patterns for production, covering online serving, offline processing, and scalable frameworks like Ray Serve, with insights from 100+ user interviews.