Large-Scale Deep Learning to Augment Production RL Workloads at Riot Games
Learn to leverage large-scale deep learning for building game bots, predicting outcomes, and optimizing training processes in game development. Explore supervised learning techniques and scaling with Ray tools.
SkyML: Infra-less Machine Learning on Any Cloud
Explore SkyML for simplified, high-performance machine learning across clouds. Leverage best-of-breed accelerators, spot instances, and automatic resource management for cost-effective ML applications.
Building and Deploying Real-World ML Models with Ray and Weights & Biases
Discover how to leverage Ray and Weights & Biases for scalable MLOps, hyperparameter optimization, and real-world ML model deployment through case studies and practical insights.
A Cross-Platform, Cross-Tool Ray on Kubernetes Deployment
Deploy Ray, Spark, Dask, and Jupyter on mixed-architecture Kubernetes clusters. Learn cross-building containers, handling Ubuntu versions, and glibc versioning for heterogeneous Ray clusters.
Ray/RLLib-Empowered Reinforcement Learning Based Recommender Systems in NetEase Game
Explore how Ray/RLLib enhances reinforcement learning-based recommender systems in NetEase Games, improving user experience and profitability. Learn about the open-source RL4RS dataset for training and evaluating RL algorithms.
Making Your Enterprise GenAI Ready and GenAI Enterprise Ready
Prepare your organization for GenAI challenges and opportunities. Learn best practices for integrating GenAI capabilities with enterprise security, privacy, and guardrails. Explore Google Cloud's innovative GenAI solutions for enterprise environments.
Enterprise Vector-based Information Retrieval and Prompt Engineering at Scale Using Open Source on AWS
Build enterprise-scale vector-based information retrieval systems using open source technologies on AWS, addressing challenges in Retrieval Augmented Generation for accurate, hallucination-free results.
Building Context-Aware Reasoning Applications with LangChain and LangSmith
Explore context-aware reasoning in LLM applications. Learn about cognitive architectures, LangChain, and LangSmith for building differentiated AI products. Gain insights on evaluation and practical techniques.
Fast LLM Serving with vLLM and PagedAttention
Explore vLLM, an open-source library for fast LLM inference using PagedAttention. Learn how it achieves higher throughput and its implementation for efficient AI model serving.
Large Language-Style Universal Models for Short Video Recommendations
Explore a ray-based solution for large-scale video recommendations, featuring a single big model with sparse inputs/outputs and universal graph search algorithm. Learn about its advantages and empirical results.
Perplexity AI: Building the World's Best LLM-Powered Search Engine in 6 Months
Explore the engineering challenges and opportunities in building Perplexity.ai, an LLM-powered answer engine, with insights from founder Aravind Srinivas on rapid development and resource optimization.
Easily Scale to Production-Ready Generative AI with NVIDIA and Anyscale
Scale generative AI to production with NVIDIA and Anyscale's partnership. Learn how their combined technologies optimize performance, lower costs, and enable secure deployment of AI services at scale.
Embeddings and Vectors: The Key to Production AI
Explore how teams program AI with data using embeddings and vectors, and learn about future approaches for building production AI applications.
Understanding the Landscape of Latest Large ML Models
Explore the evolving landscape of large ML models, from ChatGPT to open-source projects. Learn how teams use W&B to accelerate development and stay ahead in the rapidly advancing field of AI.
Large Scale Pathways Recommender Systems (PaRS) at Verizon
Explore Verizon's innovative multi-task learning recommender system, PaRS, leveraging Ray ecosystem to enhance customer experience through deep learning and efficient large-scale model training.
Enabling End-to-End LLMOps on Michelangelo with Ray
Explore Uber's extension of Michelangelo for end-to-end LLMOps, leveraging Ray for scalable development with hundreds of A100 GPUs and integrating open-source techniques for efficient custom LLM creation.
Best Practices for Productionizing Distributed Training with Ray Train
Explore fault-tolerant distributed training techniques using Ray Train, covering experiment restoration, node failure recovery, cloud storage snapshots, and large model checkpointing for efficient AI workloads.
A Ray-based ML Platform for Early Cancer Detection
Explore how Ray enhances scalability and efficiency in machine learning for early cancer detection, focusing on improved model development and evaluation for potential diagnostic applications.
Ray Train: A Production-Ready Library for Distributed Deep Learning
Explore Ray Train's architecture for efficient, cost-effective distributed deep learning. Learn about resource scheduling, API simplicity, and exclusive features for LLM training, including Distributed Checkpointing.
Building a Chat Assistant with Canopy and Anyscale Endpoints
Explore building RAG-based chat assistants using Canopy and Anyscale Endpoints. Learn architecture, see live examples, and get started with your own project using flexible frameworks and serverless LLM APIs.
Fast and Scalable Model Training with PyTorch and Ray
Learn distributed model training with PyTorch and Ray. Migrate code, scale AI workflows, and optimize performance for large-scale training and fine-tuning on Anyscale.