GitHub - cleanlab/cleanlab: The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
# cleanlab works with **any classifier**. Yup, you can use PyTorch/TensorFlow/OpenAI/XGBoost/etc. cl = cleanlab.classification.CleanLearning(sklearn.YourFavoriteClassifier()) # cleanlab finds data and label issues in **any dataset**... in ONE line of code! label_issues = cl.find_label_issues(data, labels) # cleanlab trains a robust version of your model that works more reliably with noisy data. cl
Introduction to Data-Centric AI
Introduction to Data-Centric AI IAP 2024 Typical machine learning classes teach techniques to produce effective models for a given dataset. In real-world applications, data is messy and improving models is not the only way to get better performance. You can also improve the dataset itself rather than treating it as fixed. Data-Centric AI (DCAI) is an emerging science that studies techniques to imp
Data-Centric AIの紹介
社内勉強会でAndrew Ng先生が提唱するData-Centric AIについて紹介した際の資料です。Read less
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第1回 Data-Centric AI勉強会 LT: AIドラレコを支える一貫性のあるデータの作り方
DataPerf
GitHub - ml6team/fondant: Production-ready data processing made easy and shareable
Data-centric ML benchmarking: Announcing DataPerf’s 2023 challenges
GitHub - Data-Centric-AI-Community/nist-crc-2023: NIST Collaborative Research Cycle on Synthetic Data. Learn about Synthetic Data week by week!
Data-centric ML benchmarking: Announcing DataPerf’s 2023 challenges