Building RAG-based LLM Applications for Production - LLMs III Talk
Develop and deploy scalable RAG-based LLM applications. Learn to handle data loading, preprocessing, embedding, and serving on clusters. Explore Anyscale Endpoints for cost-effective open-source model serving.
Meeting Societal Challenges: Scalable Big Data-driven, AI-enabled Approaches
Explore scalable big data-driven, AI-enabled approaches for societal challenges. Learn about innovative solutions in healthcare, agriculture, and smart cities using advanced analytics and deep learning techniques.
Legion: Programming Heterogeneous, Distributed Parallel Machines
Explore hierarchical data organization and computation in parallel programming, focusing on Legion's design for complex machine architectures.
Federated Learning Tutorial: Concepts, Applications, Challenges, and Framework
Explore federated learning concepts, applications, challenges, and frameworks. Gain insights into collaborative machine learning while preserving data privacy and security.
Federated Learning with Formal User-Level Differential Privacy Guarantees
Explore federated learning with user-level differential privacy guarantees. Discover scalable algorithms for industrial workloads in data privacy and machine learning.
Privacy-Aware Compression for Federated Learning
Explore privacy-aware compression techniques for federated learning, addressing challenges and implementing local differential privacy mechanisms to enhance data protection in collaborative AI.
MDSPAN - A Deep Dive Spanning C++, Kokkos and SYCL - Lecture 2022
Explore MDSPAN's evolution, C++ standardization, and future in SYCL. Gain insights into multidimensional array references and their impact on distributed and high-performance computing.
Distributed Ranges: A Model for Building Distributed Data Structures, Algorithms, and Views
Explore distributed ranges for building scalable data structures and algorithms across clusters. Learn to implement generic, interoperable distributed computing solutions using modern C++ features.
A Holistic Approach to Securing Spark-Based Data Engineering
Learn to secure Apache Spark-based data engineering applications. Explore security best practices, identify threats, prioritize requirements, and implement practical steps for Kubernetes and object storage environments.
Predibase: A Low-Code Deep Learning Platform Built for Scale
Explore Predibase: a low-code deep learning platform combining large-scale ML with state-of-the-art architectures for NLP, computer vision, and tabular data, built on Ludwig and Ray for scalable, end-to-end solutions.
Hugging Face and Ray AIR Integration for Scalable Machine Learning
Explore Hugging Face and Ray AIR integration for scalable model training and data loading. Learn to create end-to-end workflows from data ingest to inference and serving.
KubeRay - A Kubernetes Ray Clustering Solution
Explore KubeRay, an open-source toolkit for managing Ray clusters on Kubernetes. Learn about architectural decisions, resource management, job submission, and autoscaling capabilities.
Alpa: Simple Large Model Training and Inference on Ray
Explore Alpa, a Ray-native library for automated model-parallel training of large deep learning models, optimizing execution plans and scaling complex architectures efficiently.
SkyML: Infra-less Machine Learning on Any Cloud
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.
Building and Deploying Real-World ML Models with Ray and Weights & Biases
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.
Making Your Enterprise GenAI Ready and GenAI Enterprise Ready
Prepare your organization for GenAI challenges and opportunities. Learn best practices for integrating GenAI capabilities with enterprise security, privacy, and guardrails. Explore Google Cloud's innovative GenAI solutions for enterprise environments.
Easily Scale to Production-Ready Generative AI with NVIDIA and Anyscale
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.
Fast and Scalable Model Training with PyTorch and Ray
Learn distributed model training with PyTorch and Ray. Migrate code, scale AI workflows, and optimize performance for large-scale training and fine-tuning on Anyscale.
Scalable and Cost-Efficient AI Workloads with AWS and Anyscale
Learn to scale and productionize GenAI and LLM workloads cost-effectively using AWS compute instances and Anyscale's capabilities for ambitious AI projects.
Optimizing LLM Inference with AWS Trainium, Ray, vLLM, and Anyscale
Explore cost-effective LLM inference scaling using AWS accelerators, Ray, vLLM, and Anyscale. Learn to build a complete stack on EKS and leverage advanced cluster management for enterprise-grade GenAI workloads.
Federated Learning with Communication Constraints: Challenges and Recent Results
Explore federated learning optimization with compression, focusing on convergence-compression tradeoffs, user-heterogeneity impacts, and a novel random codebook compression scheme.