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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.
Aprenda a construir e escalar pipelines de IA de ponta a ponta usando BigDL 2.0, acelerando projetos de IA e aproveitando ao máximo os recursos de hardware disponÃveis.
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.
Explore graph technologies for AI in manufacturing, leveraging Ray for scalable solutions. Learn integration of graph models, NLP, and deep learning for industrial applications.
Seamlessly scale end-to-end AI pipelines with BigDL 2.0, exploring its capabilities for efficient and scalable artificial intelligence workflows.
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.
Explore large language model evaluation using Ray in hybrid cloud environments. Learn auto-scaling, resource management, and unified workflows for efficient multi-task assessment across diverse domains.
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.
Discover a new declarative REST API for Ray Serve, enabling seamless configuration and updates of applications within your MLOps lifecycle on Kubernetes.
Dive into Ray AIR's data processing engine for efficient ML pipeline scaling. Learn distributed data sharding, parallel I/O, GPU optimization, and autoscaling for improved performance and scalability.
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.
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.
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