Cleanlab Features

Functionality (5)

Identification: Correctly identify inaccurate, incomplete, or duplicated data from a data source.
Correction: Utilize deletion, modification, appending, merging, or other methods to correct bad data.
Normalization: Standardize data formatting for uniformity and easier data usage.
Preventative Cleaning: Clean data as it enters the data source to prevent mixing bad data with cleaned data.
Data Matching: Finds duplicates using the fuzzy logic technology or an advance search feature.

Reporting: Provide follow-up information after data cleanings through a visual dashboard or reports.
Automation: Automatically run data identification, correction, and normalization on data sources.
Quality Audits: Schedule automated audits to identify data anomalies over time based on set business rules.
Dashboard: Gives a view of the entire data quality management ecosystem.
Governance: Allows user role-based access and actions to authorization for specific tasks.

Model Training Efficiency: Enables smart selection of data for annotation to reduce overall training time and costs.
Automated Model Retraining: Allows for automatic retraining of models with newly annotated data for continuous improvement.
Active Learning Process Implementation: Facilitates the setup of an active learning process tailored to specific AI projects.
Iterative Training Loop Creation: Allows users to establish a feedback loop between data annotation and model training.
Edge Case Discovery: Provides the ability to identify and address edge cases to enhance model robustness.

Smart Data Triage: Enables efficient triaging of training data to identify which data points should be labeled next.
Data Labeling Workflow Enhancement: Streamlines the data labeling process with tools designed for efficiency and accuracy.
Error and Outlier Identification: Automates the detection of anomalies and outliers in the training data for correction.
Data Selection Optimization: Offers tools to optimize the selection of data for labeling based on model uncertainty.
Actionable Insights for Data Quality: Provides actionable insights into data quality, enabling targeted improvements in data labeling.

Model Performance Insights: Delivers in-depth insights into factors impacting model performance and suggests enhancements.
Cost-Effective Model Improvement: Enables model improvement at the lowest possible cost by focusing on the most impactful data.
Edge Case Integration: Integrates the handling of edge cases into the model training loop for continuous performance enhancement.
Fine-tuning Model Accuracy: Provides the ability to fine-tune models for increased accuracy and specialization for niche use cases.
Label Outlier Analysis: Offers advanced tools to analyze label outliers and errors to inform further model training.