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Explore Cruise.data, a novel ML data pre-processing framework combining tf.data, PyTorch, and large-scale processing capabilities for efficient and scalable dataset handling in autonomous vehicle development.
Learn to implement production-ready reinforcement learning and decision-making systems using RLlib, exploring real-world applications, challenges, and best practices.
Learn to launch elastic large-scale distributed training jobs using TorchX and Ray, overcoming traditional barriers and simplifying the transition from research to production.
Explore high-performance large-scale data shuffling with Exoshuffle in Ray, outperforming Spark and achieving near-theoretical performance for ML applications.
Accelerate ML research and prototyping using Ray's distributed computing platform. Explore Spotify's journey, infrastructure integration, and best practices for enhanced model development and experimentation.
Optimize traffic control for FIFA 2022 using RLlib-powered multi-agent reinforcement learning, microsimulation, and Graph Convolutional Networks for congestion management in Qatar.
Explore cutting-edge AI research and applications with Anca Dragan's keynote address at Ray Summit 2022, focusing on advancements in machine learning and distributed computing.
Explore efficient, flexible data pipelines using Ray Workflow. Learn to implement durable Ray tasks for ETL workloads and ML pipelines, combining workflow system advantages with Ray's data processing capabilities.
Explore Ray Serve 2.0's features, use cases, and architecture for multi-model inference and composition. Learn about autoscaling and production hardening techniques.
Explore Alpa, a Ray-native library for automated model-parallel training of large deep learning models, optimizing execution plans and scaling complex architectures efficiently.
Explore highly available serving in Ray 2.0, its architecture, functionality, and practical deployment for improved efficiency and reduced disruption during failures.
Optimize complex production schedules using multi-agent decomposition and Ray for faster decision-making in mixed integer linear programming problems.
Explore KubeRay, an open-source toolkit for managing Ray clusters on Kubernetes. Learn about architectural decisions, resource management, job submission, and autoscaling capabilities.
Explore reinforcement learning for real-time counterfactual explanations in machine learning explainability. Learn about FastCFE algorithm, OpenAI Gym, and Ray+RLlib for actionable insights in various applications.
Optimize wind farm energy production using deep reinforcement learning and large-scale simulations. Boost annual output by 1-2% through advanced yaw control strategies.
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