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Fundamentals of Neuroscience, Part 1: The Electrical Properties of the Neuron
Organic Chemistry 1
Mountains 101
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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.
Discover how to leverage Snowflake Container Runtime and Ray for efficient, scalable model training through a practical demonstration of performance optimization techniques.
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.
Discover how Ray on Kubernetes revolutionizes quantitative research with scalable infrastructure supporting 100,000 CPU cores, spot instances, and seamless GPU integration for high-performance trading operations.
Discover how Akash Network's open-source super cloud platform revolutionizes distributed computing and AI workloads, featuring real-world applications and live demonstrations of Ray clusters with vLLM and Llama-3.1.
Discover how Princeton University revolutionizes academic research computing by implementing Ray across campus clusters, multi-institutional systems, and hybrid cloud environments for enhanced ML and HPC capabilities.
Explore how LanceDB and Ray combine to scale multimodal data pipelines and LLM inference, featuring insights on zero-cost schema evolution, batch processing, and online inference with Ray Serve LLM.
Discover how to scale AI workflows using Ray and Anyscale, from fundamental concepts to hands-on implementation with distributed machine learning libraries and practical notebooks.
Discover reference architectures, stability practices, and infrastructure solutions for deploying Ray at scale, plus learn how RayTurbo accelerates production performance.
Discover how Two Sigma transforms academic AI breakthroughs into production-grade systems for quantitative finance, separating real value from industry buzz.
Explore cost-effective LLM inference scaling using AWS accelerators, Ray, vLLM, and Anyscale. Learn to build a complete stack on EKS and leverage advanced cluster management for enterprise-grade GenAI workloads.
Learn to scale and productionize GenAI and LLM workloads cost-effectively using AWS compute instances and Anyscale's capabilities for ambitious AI projects.
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.
Explore building and scaling end-to-end LLM workflows, covering data processing, model fine-tuning, evaluations, and production inference with Anyscale's modern platform.
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.
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