AIStore + HuggingFace: Distributed Downloads for Large-Scale Machine Learning| AIStore
Let your datasets roam freely between the multicloud with Tigris! Today you'll learn how to import your datasets into Tigris in a snap.| Tigris Object Storage Blog
Generating sample data based on the combination of specific parameters and options is a common use case. In this post, I illustrate utilizing a simple recursive approach for generating all possible combinations from input into a dataset.| The Code Ship
AIStore + HuggingFace: Distributed Downloads for Large-Scale Machine Learning| AIStore
Download the dataset produced during this 20-minute backpack scan of the Nepalese Peace Pagoda in Brisbane. The scan was processed, colorized, and rendered in the latest version of Emesent Aura.| Emesent
At Open Source Observer, we have been committed to building everything| Open Source Observer Blog
Authors| Chris Choy
MLCommons AAAI 2025 standardization collaboration evaluation in ai safety| MLCommons
CKAN supports the MLCommons Croissant metadata standard| MLCommons
Explores Chapter 8 of Chip Huyen's 'AI Engineering,' examining the intricate landscape of dataset engineering through the lenses of curation, augmentation, and processing.| mlops.systems
Introduction The rise of large language models (LLMs) and generative artificial intelligence (GenAI) presents new opportunities to build innovative tools and is already enabling revolutionary AI-ba…| SIGARCH
DescribeML is a Visual Studio Code plugin to precisely describe machine learning datasets.| Modeling Languages
I summarise the kinds of evaluations that are needed for a structured data generation task.| mlops.systems
I'm publishing a unique new dataset of Afghan newspaper and magazine articles from the 2006-2009 period.| mlops.systems
I published a dataset from my previous work as a researcher in Afghanistan.| mlops.systems
I share my journey of building language models for Balochi, a language with few digital resources. I discuss assembling a dataset of 2.6 million Balochi words.| mlops.systems
Benchmarks, Competitions, Datasets, Leaderboards, Machine Learning, ML4Sys, MLSys| SIGARCH
Introduction The advances in Machine Learning field have proven useful ranging from applications in Natural Language Processing to Computer Vision. These remarkably successful demonstrations have drawn high interests from the physical and biological science communities.| ChemicBook