[ Cleanlab Reviews ](https://www.g2.com/products/cleanlab/reviews) [ Cleanlab Reviews ](https://www.g2.com/products/cleanlab/reviews) # 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. Show More ##### ## Management (5) 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. Show More ##### ## Generative AI (2) AI Text Generation Allows users to generate text based on a text prompt. AI Text Summarization Condenses long documents or text into a brief summary. Show More ##### ## Model Training & Optimization - Active Learning Tools (5) 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. Show More ##### ## Data Management & Annotation - Active Learning Tools (5) 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. Show More ##### ## Model Performance & Analysis - Active Learning Tools (5) 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. 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