Data Science and Analytics Consulting

Use AI solutions from Cleanlab to improve your clients’ data and build/deploy robust models. Find errors in clients’ data, inform them which data needs review, train/deploy robust ML models, and efficiently annotate client data.
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Case StudyBerkeley Research Group, LLC (BRG)

improvement in ML model accuracy
reduction in time spent on data/ML work Results achieved using Cleanlab Studio
Quote from Steven Gawthorpe, Associate Director of Data Science at BRG:
We’ve started relying on Cleanlab to improve our ML and AI models at Berkeley Research Group LLC for over a month... I have to say, I’m impressed. Here’s what we found:
  ·  Increased model accuracy by 15%
  ·  Improved explainability & addressed performance impediments
  ·  Cut out training iterations by one-third
  ·  Overall performance improvement for our Data Science team
Berkeley Research Group is a global consulting firm that helps leading organizations advance in three key areas: disputes and investigations, corporate finance, and performance improvement and advisory.
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How Cleanlab can help your consulting team

Automatically detect issues in a client’s data such as incorrect values, anomalies, bad metadata, etc. Consider if using software to audit data for mistakes is helpful for compliance.
Efficiently annotate data for a client, or help them set up an effective data labeling pipeline.
Issue detection is based on state-of-the-art AI algorithms invented by Cleanlab scientists.
Easily train robust ML models on messy real-world datasets and deploy them in 1-click. Cleanlab uses Data-Centric AI to produce reliable models, learn more about these techniques via the free MIT course taught by the Cleanlab founders.
Fix detected data issues quickly via the Cleanlab Studio interface, or inform client which data requires careful review.
Videos on using Cleanlab Studio to find and fix incorrect labels for:
cleanlab studio automatically improves data and deploys robust models

In this tabular dataset of student grades, Cleanlab Studio automatically detected label errors, ambiguous examples, and outliers. These issues harm downstream models and analytics.


Cleanlab Studio helps you quickly fix the detected issues to get a better version of your dataset. Use the auto-corrected dataset produced by Cleanlab in place of your original dataset to get more reliable ML and analytics, without any change in your existing code!