Deploying Serverless Inference Endpoints - Setup and Cost Comparison
Deploy serverless inference endpoints efficiently. Learn setup, use cases, and cost comparisons for API-based machine learning model deployment.
Fine-tuning Multi-modal Video and Text Models
Explore fine-tuning techniques for multi-modal video and text models, covering dataset generation, clipping, querying, and evaluation using IDEFICS 2 and Jupyter Notebooks.
Top Ten Tips for Fine-tuning Large Language Models
Discover ten essential tips for effective fine-tuning of language models, from starting small to advanced techniques like LoRA and preference tuning. Gain practical insights for optimal results.
Running ML Workloads with AWS Purpose-Built ML Accelerators and Ray
Discover AWS Trainium and Inferentia ML accelerators for high-performance, cost-effective Generative AI in the cloud. Explore native support for these accelerators in Ray.
Tend and Tune Your MARL Experiments with Ray and Weights & Biases
Optimize multi-agent reinforcement learning experiments using Ray and Weights & Biases. Automate tuning, trace experiments, and centralize data for faster, more efficient results in scenarios like autonomous driving and drone flying.
What's New in RLlib 2.0 - Key Improvements and Features
Explore RLlib 2.0's improvements: configuration, algorithms, customization, policy serving, and Ray AIR integration for intuitive and performant reinforcement learning.
Wind Farm Optimization Using Reinforcement Learning and Large-Scale Simulation
Optimize wind farm energy production using deep reinforcement learning and large-scale simulations. Boost annual output by 1-2% through advanced yaw control strategies.
Accelerating Scientific Discovery with AI and Quantum Computing
Explore cutting-edge insights on distributed computing and AI from IBM's Dario Gil in this Ray Summit 2022 keynote address.
Production RL and Decision-Making with RLlib
Learn to implement production-ready reinforcement learning and decision-making systems using RLlib, exploring real-world applications, challenges, and best practices.
Practical Data Considerations for Building Production-Ready LLM Applications
Discover practical tips for managing data in scalable LLM systems. Learn how LlamaIndex and Ray provide tools for building robust, production-ready AI applications using private data.
Llama - Scaling Up LLMs in an Open Ecosystem
Explore Llama's evolution, its impact on Generative AI, and learn how to leverage state-of-the-art performance using Ray-based platforms for building with open-source large language models.
Optimizing LLM Training with Airbnb's Next-Gen ML Platform
Explore Airbnb's ML platform upgrade for efficient LLM use, featuring Ray integration for improved developer productivity, performance, and cost-effective GPU utilization on AWS.
Managed Cloud Infrastructure for LLMs
Explore solutions for LLM infrastructure challenges, including cost reduction, GPU availability, scalability, and cloud resource management. Learn to accelerate AI development and deployment.
Simplified Training, Tuning and Deployment of Foundational Models as a Service with Red Hat OpenShift
Simplify end-to-end lifecycle of foundation models using Red Hat OpenShift Data Science. Learn about cloud-native, scalable stack for training, tuning, and inferencing with integrated open-source components.
OneDigital ML Platform: Unleashing the Power of Machine Learning at Scale
Explore OneDigital's journey in building a scalable ML platform using Ray and Anyscale. Learn about architecture, strategies, and retail use cases for advanced ML models.
KubeRay: A Ray Cluster Management Solution on Kubernetes
Explore KubeRay's integration with Kubernetes, its capabilities for Ray cluster management, and performance benchmarks for simplified deployment of Ray applications.
Scaling AI Health Assistants: Challenges and Solutions
Explore challenges and solutions in scaling AI health assistants, focusing on model management, state persistence, and system reliability. Learn key principles for building scalable AI products using Ray.
Lessons From Fine-Tuning Llama-2
Explore insights from fine-tuning Llama-2 for specialized applications. Learn setup, infrastructure, and efficiency techniques to outperform GPT-4 in specific scenarios using Anyscale and Ray.
Developing and Serving RAG-Based LLM Applications in Production
Learn to develop, evaluate, and scale RAG-based LLM applications for production, including advanced topics like hybrid routing to bridge the gap between open-source and closed LLMs.
Asymptotic Analysis for Selection-Mutation Models: Locations and Weights of Limit Singular Measures
Explore asymptotic analysis of selection-mutation models, focusing on limit singular measures' locations and weights. Gain insights into population dynamics and mathematical biology.
A Spline Tour of Deep Learning: The Scattering Way
Explore deep learning's progress, challenges, and theoretical foundations. Gain insights into the geometric interpretation of deep networks and spline approximation, with a focus on Scattering Networks.