Scale and Accelerate Distributed Model Training in Kubernetes Clusters
Orchestrate and accelerate distributed deep learning workloads across multiple GPUs and nodes using Kubernetes, Kubeflow PytorchJob, RDMA, RoCE, and SRIOV for near-linear performance scaling.
Shopify's Machine Learning Platform Journey: Building Merlin with Open Source Tools
Explore Shopify's journey in building Merlin, an open-source ML platform. Gain insights into the architecture, technologies, and tools used to scale machine learning work effectively.
Introduction to Model Deployment with Ray Serve
Learn to deploy ML models using Ray Serve. Explore Ray Core APIs, create scalable architectures, and integrate with MLflow and FastAPI. Gain hands-on experience in distributed computing and model deployment.
Best Practices in HPC and HTC Environments
Discover best practices for optimizing performance in High Performance and High Throughput Computing environments.
Ray.jl - Julia Runtime and Client for the Ray Compute Framework
Explore Ray.jl, a Julia client for the Ray compute framework. Learn about distributed computing, implementation challenges, and real-world applications in scaling workloads.
Anyscale's Governance Suite for Scalable AI Platforms - Ray Summit 2024
Explore Anyscale's Governance Suite for AI platforms, addressing security, controls, and productivity challenges in the era of Generative AI. Gain insights into sustainable AI practices for modern enterprises.
Roblox's Journey to Supporting Multimodality on vLLM - Ray Summit 2024
Explore Roblox's integration of multimodal language models into vLLM, uncovering technical challenges and insights for developers seeking to enhance their LLM deployments with advanced AI capabilities.
ByteDance's Platform for Reinforcement Learning from Human Feedback
Explore ByteDance's journey in building a video data processing pipeline using Ray's ecosystem for creating realistic video generation models from text instructions.
Uber's Journey in Building Multi-Modal Foundation Models
Dive into Uber's approach to developing in-house multi-modal foundation models, optimizing earner onboarding and screening processes globally using Ray for distributed training and deployment.
Klaviyo's Journey to Robust Model Serving with Ray Serve
Explore Klaviyo's journey in building DART, a model serving platform using Ray Serve. Gain insights into architecture, deployment strategies, and practical advice for implementing Ray Serve in your own projects.
Faster Model Serving with Ray and Anyscale - Ray Summit 2024
Explore Ray Serve's capabilities for distributed model serving and deployment, focusing on Anyscale's solutions for large-scale AI models and the challenges of building AI applications in the era of generative AI.
Fine-Tuning Large Language Models at Scale - Workday's Approach
Dive into Workday's innovative approach to fine-tuning LLMs securely in a multi-tenant environment, addressing GPU scarcity and data access controls using PEFT techniques and KubeRay's autoscaling capabilities.
Scaling Inference Deployments with NVIDIA Triton Inference Server and Ray Serve
Explore the integration of Ray Serve and NVIDIA Triton Inference Server for enhanced ML model performance and scalability. Learn to optimize inference deployments using advanced tools and a new Python API.
Handshake's Approach to Content Tagging with vLLM and Anyscale
Explore Handshake's journey in implementing vLLM-based content tagging using Anyscale, enhancing job feed performance and addressing growing LLM requirements through innovative internal tools and methodologies.
The State of vLLM - Advancements in LLM Inference and Serving
Explore the latest advancements in vLLM, the open-source LLM inference engine. Gain insights into its growing adoption, new features, performance improvements, and future roadmap for efficient LLM deployment.
Accelerated LLM Inference with Anyscale - Ray Summit 2024
Dive into Anyscale's latest LLM enterprise features and open-source contributions. Explore advancements in vLLM, including FP8 support and speculative decoding, doubling throughput and latency efficiency for improved inference performance.
Building Scalable AI Agents with Ray
Explore Ragent, a framework for scalable AI agents built on Ray's distributed computing capabilities. Learn about its key features and integration with popular tools for developing production-ready agent applications.
Anyscale's Vision and Ray AI Compute Engine - Future of AI Scaling
Discover Anyscale's vision for AI scaling with Ray, the AI Compute Engine. Explore cutting-edge solutions tackling AI workload management challenges, from compiled graphs to advanced governance suites.
Transforming the Physical World with AI - Samsara's Journey in Fleet Management Technology
Explore how Samsara leverages Ray and Anyscale to revolutionize AI in fleet management. Discover the impact of AI across their entire pipeline, from training to deployment, and its transformative effects on physical operations.
Hadoop Tutorials
Learn Big Data and Hadoop essentials, including HDFS, MapReduce, YARN, Hive, and HBase. Explore career paths, architecture, and practical problem-solving in Hadoop ecosystems.
Hadoop Tutorials for Beginners
Comprehensive introduction to Big Data technologies, covering HDFS, MapReduce, Hive, Pig, Spark, NoSQL, HBase, Sqoop, Flume, and Kafka. Ideal for beginners seeking a solid foundation in Hadoop ecosystem.