MACHINE LEARNING
NVIDIA's HENS Revolutionizes Extreme Weather Prediction Without Supercomputers
NVIDIA, in collaboration with Berkeley Lab, introduces HENS, a machine learning tool for predicting extreme weather, offering supercomputer-class forecasting with reduced computational power and cost.
Exploring Systematic Feature Discovery in Digital Asset Markets
Glassnode introduces a new framework for feature discovery in digital asset markets, utilizing machine learning to identify high-value indicator combinations.
Together AI Enhances Fine-Tuning Platform with Larger Models and Hugging Face Integration
Together AI unveils major upgrades to its Fine-Tuning Platform, including support for 100B+ parameter models, extended context lengths, and improved integration with Hugging Face Hub.
NVIDIA Unveils Nemotron Nano 2 9B for Enhanced Edge AI Performance
NVIDIA's new Nemotron Nano 2 9B model offers superior accuracy and efficiency for edge AI applications, featuring a hybrid architecture and configurable thinking budget.
Dynamo 0.4 Enhances AI Model Deployment with Faster Performance and Advanced Autoscaling
Dynamo 0.4 introduces significant advancements in AI model deployment, offering 4x faster performance, SLO-based autoscaling, and real-time observability, enhancing efficiency and scalability.
NVIDIA's Grace Hopper Superchip Revolutionizes XGBoost 3.0 for Terabyte-Scale Datasets
NVIDIA's latest Grace Hopper Superchip enhances XGBoost 3.0, enabling efficient processing of terabyte-scale datasets with improved speed and cost-effectiveness.
OpenAI and NVIDIA Collaborate to Enhance AI with New Open Models
OpenAI and NVIDIA have launched new AI models optimized for NVIDIA's infrastructure, promising advancements in AI development and application across various industries.
Enhancing ML Models in Semiconductor Manufacturing with NVIDIA CUDA-X
NVIDIA's CUDA-X Data Science libraries optimize feature engineering in semiconductor manufacturing, enhancing ML model performance and reducing ETL processing time by up to 40%.
RAPIDS Introduces GPU Polars Streaming and Unified GNN API Enhancements
NVIDIA's RAPIDS suite version 25.06 unveils new features including GPU Polars streaming, a unified GNN API, and zero-code ML speedups, enhancing Python data science capabilities.
NVIDIA Unveils Data Flywheel Blueprint to Optimize AI Agents
NVIDIA introduces the Data Flywheel Blueprint, a workflow aimed at enhancing AI agents by reducing costs and improving efficiency using automated experimentation and self-improving loops.
GitHub Explores AI and ML: Insights and Best Practices
GitHub Blog delves into AI and ML, offering insights from engineers and thought leaders on concepts, techniques, and real-world applications.
NVIDIA Advances ML in Manufacturing with CUDA-X Data Science
NVIDIA leverages CUDA-X data science to optimize chip manufacturing workflows, addressing challenges like dataset imbalance and enhancing model performance.
NVIDIA Enhances LLMOps for Efficient Model Evaluation and Optimization
NVIDIA introduces advanced LLMOps strategies to tackle challenges in large language model deployment, focusing on fine-tuning, evaluation, and continuous improvement, as demonstrated in collaboration with Amdocs.
NVIDIA's cuML Enhances Tree-Based Model Inference with Forest Inference Library
NVIDIA's cuML 25.04 introduces enhancements to the Forest Inference Library, boosting tree-based model inference performance with new features and optimizations.
NVIDIA RAPIDS Enhances Machine Learning with Zero-Code Acceleration and Performance Gains
NVIDIA's RAPIDS introduces zero-code acceleration for machine learning, boosts IO performance, and supports out-of-core XGBoost training, streamlining data science workflows.