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Explore the evolving landscape of large ML models, from ChatGPT to open-source projects. Learn how teams use W&B to accelerate development and stay ahead in the rapidly advancing field of AI.
Explore Ray Data's fast, flexible, and scalable data loading for ML training. Learn how it matches PyTorch and TensorFlow performance while offering advanced features for scale and diverse data types.
Explore how Niantic leverages Ray to build precise AR maps, streamline research-to-production pipelines, process user scans efficiently, and create complex 3D environments for immersive experiences.
Explore how teams program AI with data using embeddings and vectors, and learn about future approaches for building production AI applications.
Explore serverless computing with Ray and Knative. Compare approaches, uncover best practices, and learn about potential pitfalls in serverless development for machine learning and HTTP services.
Scale generative AI to production with NVIDIA and Anyscale's partnership. Learn how their combined technologies optimize performance, lower costs, and enable secure deployment of AI services at scale.
Discover how Ray Serve optimizes LLM deployment, reducing costs through fine-grained autoscaling, continuous batching, and model parallel inference. Learn to easily deploy Hugging Face models with these optimizations.
Explore LinkedIn's journey in serving complex ML workflows using Ray-Serve, addressing AI application complexity and enhancing AI engineer productivity.
Explore Llama 2 models' strengths and weaknesses compared to ChatGPT, examining quality, cost, and usability. Learn when to consider open source LLMs and available options for AI development.
Scale computer vision models efficiently using Ray's distributed training framework. Compare performance with Kubeflow, leveraging cost-effective S3 storage and optimized data processing for improved speed and GPU utilization.
Explore Ray Serve's capabilities for efficient ML model deployment, including scalability, high availability, and cost optimization. Watch a live demo of serving an ML application on the Anyscale platform.
Explore SkyPilot, an open-source framework for running AI and batch jobs across clouds. Learn how it maximizes GPU availability, reduces costs, and simplifies cloud-agnostic execution for AI practitioners.
Explore Ray Data streaming for scaling ML pipelines across CPU/GPU clusters. Learn to optimize preprocessing, training, and inference workflows in distributed environments.
Explore the engineering challenges and opportunities in building Perplexity.ai, an LLM-powered answer engine, with insights from founder Aravind Srinivas on rapid development and resource optimization.
Explore Instacart's ML Training Platform built on Ray, featuring standard runtimes and advanced capabilities for distributed deep learning models using Ray AIR and KubeRay.
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