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Cleanlab Features
What are the features of Cleanlab?
Functionality
- Identification
- Correction
- Normalization
- Preventative Cleaning
- Data Matching
Management
- Reporting
- Automation
- Quality Audits
- Dashboard
- Governance
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Cleanlab Categories on G2
Filter for Features
Functionality
Identification | Correctly identify inaccurate, incomplete, or duplicated data from a data source. 11 reviewers of Cleanlab have provided feedback on this feature. | 91% (Based on 11 reviews) | |
Correction | As reported in 11 Cleanlab reviews. Utilize deletion, modification, appending, merging, or other methods to correct bad data. | 91% (Based on 11 reviews) | |
Normalization | As reported in 11 Cleanlab reviews. Standardize data formatting for uniformity and easier data usage. | 89% (Based on 11 reviews) | |
Preventative Cleaning | Clean data as it enters the data source to prevent mixing bad data with cleaned data. 11 reviewers of Cleanlab have provided feedback on this feature. | 85% (Based on 11 reviews) | |
Data Matching | Finds duplicates using the fuzzy logic technology or an advance search feature. 11 reviewers of Cleanlab have provided feedback on this feature. | 89% (Based on 11 reviews) |
Management
Reporting | Provide follow-up information after data cleanings through a visual dashboard or reports. This feature was mentioned in 11 Cleanlab reviews. | 91% (Based on 11 reviews) | |
Automation | Based on 11 Cleanlab reviews. Automatically run data identification, correction, and normalization on data sources. | 91% (Based on 11 reviews) | |
Quality Audits | Based on 10 Cleanlab reviews. Schedule automated audits to identify data anomalies over time based on set business rules. | 90% (Based on 10 reviews) | |
Dashboard | Gives a view of the entire data quality management ecosystem. This feature was mentioned in 11 Cleanlab reviews. | 94% (Based on 11 reviews) | |
Governance | Based on 11 Cleanlab reviews. Allows user role-based access and actions to authorization for specific tasks. | 88% (Based on 11 reviews) |
Generative AI
AI Text Generation | Allows users to generate text based on a text prompt. | Not enough data | |
AI Text Summarization | Condenses long documents or text into a brief summary. | Not enough data |
Model Training & Optimization - Active Learning Tools
Model Training Efficiency | Enables smart selection of data for annotation to reduce overall training time and costs. | Not enough data | |
Automated Model Retraining | Allows for automatic retraining of models with newly annotated data for continuous improvement. | Not enough data | |
Active Learning Process Implementation | Facilitates the setup of an active learning process tailored to specific AI projects. | Not enough data | |
Iterative Training Loop Creation | Allows users to establish a feedback loop between data annotation and model training. | Not enough data | |
Edge Case Discovery | Provides the ability to identify and address edge cases to enhance model robustness. | Not enough data |
Data Management & Annotation - Active Learning Tools
Smart Data Triage | Enables efficient triaging of training data to identify which data points should be labeled next. | Not enough data | |
Data Labeling Workflow Enhancement | Streamlines the data labeling process with tools designed for efficiency and accuracy. | Not enough data | |
Error and Outlier Identification | Automates the detection of anomalies and outliers in the training data for correction. | Not enough data | |
Data Selection Optimization | Offers tools to optimize the selection of data for labeling based on model uncertainty. | Not enough data | |
Actionable Insights for Data Quality | Provides actionable insights into data quality, enabling targeted improvements in data labeling. | Not enough data |
Model Performance & Analysis - Active Learning Tools
Model Performance Insights | Delivers in-depth insights into factors impacting model performance and suggests enhancements. | Not enough data | |
Cost-Effective Model Improvement | Enables model improvement at the lowest possible cost by focusing on the most impactful data. | Not enough data | |
Edge Case Integration | Integrates the handling of edge cases into the model training loop for continuous performance enhancement. | Not enough data | |
Fine-tuning Model Accuracy | Provides the ability to fine-tune models for increased accuracy and specialization for niche use cases. | Not enough data | |
Label Outlier Analysis | Offers advanced tools to analyze label outliers and errors to inform further model training. | Not enough data |
