Overcoming Distributed ML Challenges with Ray Train
Unlock efficient distributed ML pipelines with Ray Train. Explore solutions for setup, integration, debugging, fault tolerance, and data processing challenges in scalable machine learning workflows.
Stream Processing and New Approaches: Edge Processing
Explore stream processing on the edge for real-time data analysis, enabling rapid anomaly detection and distributed machine learning using open-source software.
Accelerate Your GenAI Model Inference with Ray and Kubernetes
Explore serving large GenAI models efficiently using KubeRay on Kubernetes with hardware accelerators. Learn to scale workloads, optimize performance, and reduce costs for production-ready AI inference.
Multi-party Computation I - Session 109
Explore the fundamentals and applications of Multi-party Computation, a cryptographic protocol enabling secure collaborative computations while preserving data privacy.
SHERPA: Explainable Robust Algorithms for Privacy-Preserved Federated Learning in Future Networks
Explore robust algorithms for privacy-preserved federated learning in future networks, focusing on explainable and secure data collaboration techniques.
Oort - Efficient Federated Learning via Guided Participant Selection
Efficient federated learning system that improves model training and testing by intelligently selecting participants based on data utility and device capabilities, enhancing accuracy and performance.
KungFu - Making Training in Distributed Machine Learning Adaptive
KungFu: adaptive distributed machine learning library for TensorFlow. Enables real-time parameter tuning using high-level policies, efficient monitoring, and asynchronous communication for improved training performance.
Scaling Distributed Machine Learning with the Parameter Server
Explore a framework for distributed machine learning using parameter servers, enabling scalable processing of petabyte-scale data with billions of examples and parameters across various algorithms.
Distributed Machine Learning 101 Using Apache Spark from a Browser
Explore introductory machine learning concepts and their application at scale using Apache Spark, with hands-on examples in interactive notebooks for real-world distributed data processing.
PPML 2020 - Welcome and Invited Talk - Contributed Talks
Explore cutting-edge research in privacy-preserving machine learning, including split learning, secure inference, fairness certification, crypto-oriented neural architectures, and privacy-preserving decision trees.
Accelerating Distributed MoE Training and Inference with Lina
Explore Lina, an innovative system for accelerating distributed Mixture of Experts training and inference, addressing all-to-all communication bottlenecks in large-scale AI models.
Federated Learning in Production: Enabling AI Training with Data Privacy
Explore federated learning's applications, benefits, and implementation requirements for AI training on edge devices with data privacy.
Federated Learning in Production: Enabling AI Training with Data Privacy
Explore federated learning's applications, benefits, and implementation requirements for AI training on edge devices with data privacy.
Learning and Unlearning Your Data in Federated Settings
Explore federated learning and unlearning approaches for collaborative AI model training while preserving data privacy. Analyze methods for removing data inputs in distributed settings to comply with regulations.
Conspirator - SmartNIC-Aided Control Plane for Distributed ML Workloads
Innovative control plane design using SmartNICs to optimize distributed ML workloads, reducing bottlenecks and improving resource utilization for enhanced performance and efficiency.
ASTRA-sim and Chakra - Co-design Exploration for Distributed Machine Learning Platforms
Explore co-design methodologies for distributed ML platforms using ASTRA-sim and Chakra frameworks, focusing on system optimization and performance analysis.