resources.md

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General resources related to machine learning and data-centric AI that we recommend. For additional resources on the topics covered in lectures, see the references in individual lecture notes.

Open-Source Software Tools for Data-Centric AI

• cleanlab - automatically detect problems in a dataset to facilitate ML with messy, real-world data
• refinery - assess and maintain natural language data
• great expectations - validate, document, and profile data for quality testing
• ydata-profiling - generate summary reports of tabular datasets stored as pandas DataFrame
• cleanvision - automatically detect low-quality images in computer vision datasets
• albumentations - data augmentation for computer vision
• label-studio - interfaces to label and annotate data for many ML tasks
• llamaindex - a data framework for LLM applications (Retrieval-Augmented Generation)
• dspy - algorithmically optimize LLM prompts and bootstrap data

Short Articles

• Unbiggen AI
• Andrew Ng Launches A Campaign For Data-Centric AI
• Tips for a Data-Centric AI Approach
• Data-Centric Approach vs Model-Centric Approach in Machine Learning
• A Linter for ML Datasets
• Handling Mislabeled Data to Improve Your Model
• Catch bad data in LLM datasets

Papers

• A Data Quality-Driven View of MLOps
• Advances in Exploratory Data Analysis, Visualisation and Quality for Data Centric AI Systems
• A Survey on Data Selection for Language Models

Books

• Human-in-the-Loop Machine Learning: Active Learning and Annotation for Human-centered AI
• Best Practices in Data Cleaning: A Complete Guide to Everything You Need to Do Before and After Collecting Your Data

Links

• Data-centric AI Resource Hub
• Label Errors in ML Benchmarks
• RedPajama 30 Trillion Token Text Corpus with Data Quality Measures
• Data-Centric AI Competition 2023