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Discover how to make large-scale Ray training workloads observable and reliable using Datadog's proven techniques for monitoring, debugging, and optimizing distributed AI systems.
Discover how Coinbase built secure, production-grade LLM services using Ray, vLLM & LiteLLM for one of the world's most trusted crypto exchanges with distributed scaling & authentication.
Discover how DataRobot's syftr framework uses Ray to optimize agentic LLM pipelines through massive distributed search, finding workflows 9× cheaper while maintaining accuracy.
Discover how AWS leverages vLLM for large-scale LLM inference, featuring deployment strategies, multi-accelerator clusters, and open-source contributions to optimize performance and costs.
Discover how Sutro optimizes vLLM for massive-scale batch inference, covering custom implementation layers, performance profiling, and cost attribution for predictable synthetic data generation.
Discover how Intel Xeon 6 processors deliver cost-effective AI inference for enterprise applications, featuring AMX acceleration, enhanced memory bandwidth, and Ray integration.
Discover how KubeRay unifies GenAI model lifecycles—from training to deployment—eliminating complex scripts and manual setup for streamlined ML workflows on Kubernetes.
Explore how data and model infrastructure are converging in the GenAI era, examining the blending boundaries between SQL engines and Python frameworks for unified AI-driven systems.
Discover how Apple engineers use Ray Serve as a framework-agnostic foundation for deploying scalable AI agents with multi-step reasoning, dynamic execution, and production resilience.
Master distributed training techniques using PyTorch and Ray to scale ML models from single-GPU to massive clusters with DDP, ZeRO, and FSDP methods.
Master distributed LLM fine-tuning across GPU clusters using FSDP, DeepSpeed, and Ray with hands-on orchestration and memory management strategies for frontier-scale models.
Discover how Ray addresses the shift from I/O-bound to compute-bound AI systems, providing distributed execution for scalable, fault-tolerant Python workloads across CPUs and GPUs.
Explore open-source AI infrastructure foundations with industry leaders discussing vLLM, PyTorch, and Kubernetes collaboration, standards, and community-driven innovation.
Explore how Physical Intelligence develops general-purpose AI models to enable any robot to perform any task, tackling hardware, software, and movement challenges.
Explore Ray's evolution, unified AI compute platforms, and major product updates including PyTorch Foundation integration, Azure partnership, and robotics advancements from industry leaders.
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