HOW CLEANLAB CAN HELP

Improve business analytics

Cleanlab’s AI automatically detects incorrect values and other issues lurking in your dataset (outliers, near duplicates, low-quality examples, non-IID sampling, etc). This includes errors in associated metadata (e.g. annotations or tags for images/documents).

Cleanlab Studio can be used to improve E-commerce websites, product listings, and analytics. Finding and fixing errors in product descriptions/metadata can be entirely automated, and improves: customer experience, product discoverability, SEO, advertising, as well as analytics/decision-making.

Read more: Enhancing Product Analytics and E-commerce with Cleanlab Studio

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HOW IT WORKS

How Cleanlab can improve your data analysis
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Auto-detect Issues

Automatically detect violations of key statistical assumptions like IID-sampling (e.g. if the data are drifting over time). Such violations may invalidate many data-driven conclusions.

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Audit All Types of Datasets

Audit data stored in many file formats: Excel, CSV, JSON, etc. including data with many raw text fields or images.

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Detect Outliers and Anomalies

Automatically discover outliers (anomalies) which may have an outsized impact on data-driven conclusions and should be handled with care.

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Train and Produce Reliable Models

Use Cleanlab AutoML to train and deploy state-of-the-art ML models in one 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.  

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Assessing Multiple Data Annotators

Effectively analyze crowdsourced datasets in a robust manner, and estimate which examples require additional review and which annotators are best/worst overall. Summarize overall patterns in data errors to better understand where they stem from and how they might affect conclusions.

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Resources and Tutorials

Videos on using Cleanlab Studio to find and fix incorrect labels for: text annotation or metadata, image annotation or metadata, and data tables.

CASE STUDIES

Google used Cleanlab to estimate how often its assistant devices mis-respond to the wake-word “Hey Google”.

Amazon used Cleanlab to estimate how often its assistant devices mis-respond to the wake-word “Alexa”.

Start learning with step-by-step tutorials
AI to Detect Data Entry Errors (and Impute Missing Values) in any Tabular Dataset
Programmatically identify erroneous values (or impute missing values).
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Understanding Your Data
Utilize Filters and Analytics to better resolve label issues and process errors.
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How to Automatically Create Multi-Label Datasets
Format a multi-label dataset, walk through improve data quality, and export your cleaned dataset for downstream use.
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Related Applications
Foundation and Large Language Models
Boost fine-tuning accuracy and reduce time spent.
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Data Annotation and Crowdsourcing
Label data efficiently and accurately, understand annotator quality.
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Data Entry, Management, and Curation
AI expert review of your data stores to find errors or incorrect labels.
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Customer Service
Fix common issues in customer data, and deploy robust ML models to better understand customers and handle their requests.
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Content Moderation
Train more accurate content moderation models in less time.
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