Data Entry, Management, and Curation
Case StudyThe Stakeholder Company (TSC)
We used Cleanlab to quickly validate one of our classifier models’ predictions for a dataset. This is typically a very time-consuming task since we would have to check thousands of examples by hand. However, since Cleanlab helped us identify the data points that were most likely to have label errors, we only had to inspect an eighth of our dataset to see that our model was problematic. We later realized that this was due to a post-processing error in the dataset — something that would otherwise have taken a much longer time to notice.
HOW OUR AI HELPS WITH DATA ENTRY, MANAGEMENT, AND CURATION
Videos on using Cleanlab Studio to find and fix incorrect values in:
Summarize overall patterns in data errors to better understand where they stem from and how they might affect conclusions.
Audit data stored in many file formats: Excel, CSV, JSON, etc. including data with many raw text fields or images.
Reconcile conflicting decisions made by multiple data entry workers and discover which workers are best/worst overall. Learn more.
Use Cleanlab AutoML to train and deploy state-of-the-art ML models in 1-click. Robustly train models on cleaned data to predict any information recorded in your dataset, no Machine Learning expertise required! This can help with missing value imputation and other tasks involving incomplete information.
Read about how real-world datasets are full of errors.
Learn about automatic error detection for multi-label data (e.g. image/document tagging).
Automatically discover outliers (anomalies) lurking in any dataset. Learn more.
Detect low-quality examples in any image dataset. Learn more.
