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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.
Explore solutions for LLM infrastructure challenges, including cost reduction, GPU availability, scalability, and cloud resource management. Learn to accelerate AI development and deployment.
Learn to serve large language models efficiently using KubeRay on TPUs. Explore increased performance, improved scalability, reduced costs, and enhanced monitoring for AI workloads.
Explore integrating Ray with CodeFlare and RHODS Pipelines to automatically scale foundation model workflows on OpenShift, enabling efficient parallel execution and management of compute-intensive tasks.
Explore Airbnb's ML platform upgrade for efficient LLM use, featuring Ray integration for improved developer productivity, performance, and cost-effective GPU utilization on AWS.
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.
Explore Llama's evolution, its impact on Generative AI, and learn how to leverage state-of-the-art performance using Ray-based platforms for building with open-source large language models.
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.
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.
Discover practical tips for managing data in scalable LLM systems. Learn how LlamaIndex and Ray provide tools for building robust, production-ready AI applications using private data.
Explore best practices for adapting Ray.io to meet Enterprise requirements in Google Cloud, focusing on security, networking, and integration with GKE for scalable AI/ML workloads.
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.
Explore time-series forecasting at scale and distributed data processing with Ray. Learn from Nixtla and Daft experts about leveraging Ray for massive data crunching and complex DataFrame operations.
Explore Ray Serve, on-demand scaling, and NLP project bootstrapping techniques. Learn about model serving, API controllers, and leveraging shortcuts for efficient NLP development.
Explore large-scale deep learning challenges at Uber and learn how Ray addresses infrastructure hurdles in compute, network, and storage for ML applications.
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