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CleanLab: Best ML Modules Optimizer
What do you like best about the product?
The best part of Cleanlab is it's AI models which optimizes any pretrained modules with great level of efficiency. Another best part is it's documentation, Any type of users can use Cleanlab by reading it's documentation. And TLM module is best, it optimizes any LLM. It's API feature helps the integration part much easier.
What do you dislike about the product?
As of now I find it a bit hard to dislike such great module. But still talking about it's dislike : It is expensive and some small startups may not afford it. Also, TLM doesn't do great with unstructured data.
What problems is the product solving and how is that benefiting you?
I work as a Data Manager in a Company which works with US Healthcare Data. We train modules on Healthcare datasets. Cleanlab helps us identifies and flags incorrect labels. The modules we train sometimes misinterpret the inputs. Here Cleanlab plays a vital role. This optimizes our ML modules and also helps to identifies outliers. In general Cleanlab helps us with optimizing our AI models.
Powerful label-cleaning with a slight learning curve
What do you like best about the product?
Accurate error detection. The ability to automatically spot mislabeled and low-confidence examples has saved me countless hours of manual review.
Seamless pandas integration. Working directly on DataFrames makes it trivial to plug Cleanlab into existing preprocessing pipelines.
Clear, example-driven docs. The step-by-step tutorials helped me get up and running in under an hour.
Seamless pandas integration. Working directly on DataFrames makes it trivial to plug Cleanlab into existing preprocessing pipelines.
Clear, example-driven docs. The step-by-step tutorials helped me get up and running in under an hour.
What do you dislike about the product?
Initial setup complexity. Installing all dependencies (and configuring environments) can feel a bit involved if you’re just experimenting.
Performance on very large datasets. Label-error detection can be slow without additional tuning or sampling.
Performance on very large datasets. Label-error detection can be slow without additional tuning or sampling.
What problems is the product solving and how is that benefiting you?
Cleanlab tackles the hidden “label noise” in your datasets—mislabeled, ambiguous or low-confidence examples that quietly drag down model accuracy. By automatically flagging and ranking these problematic records (and even suggesting which labels to trust), Cleanlab lets me:
Catch mistakes early, before they poison training, so my models learn from clean, reliable data.
Streamline data audits, turning hours of manual review into minutes of focused corrections.
Boost final performance, since models trained on higher-quality labels consistently deliver better accuracy and robustness.
Overall, Cleanlab empowers me to maintain a trustworthy, production-ready dataset with far less effort—and to iterate on models faster and with greater confidence.
Catch mistakes early, before they poison training, so my models learn from clean, reliable data.
Streamline data audits, turning hours of manual review into minutes of focused corrections.
Boost final performance, since models trained on higher-quality labels consistently deliver better accuracy and robustness.
Overall, Cleanlab empowers me to maintain a trustworthy, production-ready dataset with far less effort—and to iterate on models faster and with greater confidence.
Best and easy to use AI
What do you like best about the product?
Easy to use. No much hardware setup is required and the way it helps in refining data & on the e-commerce side is wonderful.
What do you dislike about the product?
Nothing as such I can think of. need to look more into the product before making any statement.
What problems is the product solving and how is that benefiting you?
So we have a lot of customer data but it's mostly messy and not linked properly but with Cleanlab it gives us a properly formatted data.
The AI tools to easy my job to clean data from row to smart data set and help our team
What do you like best about the product?
The time we spent in dataset to significanty decrese after using cleanlab. i would say its save lots of time.
What do you dislike about the product?
sometime it getting slow on large dataset but we have not so frequnt those dataset but yes there is need to improvment.
What problems is the product solving and how is that benefiting you?
The problem with our existing dataset is clean by manully most of time and sometime heuristics so this is best suited for us to solve our problem. our 70-80% time and human affort are decrese.
Powerful Tool
What do you like best about the product?
Cleanlab automatically finds and fixes errors in any dataset.
What do you dislike about the product?
None come to mind.Everything looks good and great.
What problems is the product solving and how is that benefiting you?
Fixing the errors in datasets.
Useful product
What do you like best about the product?
The AI insights which are there which helps the person do the work in less time.
What do you dislike about the product?
The delay time is quite high sometimes it takes bit more time than usual
What problems is the product solving and how is that benefiting you?
It is solving the problems related to Machine learning datasets.
Cleanlab Review: Enhancing Data Quality with AI-Powered Error Detection
What do you like best about the product?
Automated Data Cleaning
Seamless Integration
Robust Error Detection
Improves Model Accuracy
Support Multiple Data Types
Seamless Integration
Robust Error Detection
Improves Model Accuracy
Support Multiple Data Types
What do you dislike about the product?
Computational Overhead
Steep Learning Curve
Dependence on Model Predictions
Not Fully Automated
Limited GUI Support
Steep Learning Curve
Dependence on Model Predictions
Not Fully Automated
Limited GUI Support
What problems is the product solving and how is that benefiting you?
Mislabeled Data Detection
Data Quality Assurance
Reduce Manual Effort
Works Across Data Types
Scalability in Data Cleaning
Data Quality Assurance
Reduce Manual Effort
Works Across Data Types
Scalability in Data Cleaning
must have in your tool kit
What do you like best about the product?
lot of functionalities to play around with . That helps massively as we crunch data sets.very helpful community and support model
What do you dislike about the product?
difficult to use but very handy once you get hang of it. Docuemntation is improving. The community is growing .
What problems is the product solving and how is that benefiting you?
fixing data gaps in my analysis.
Lot of times back testing data sets it's critical to ensure that the data quality is good
Lot of times back testing data sets it's critical to ensure that the data quality is good
amazing starting point for dataset curation
What do you like best about the product?
clean api, very easy integration tutorials, now is one of my go-to when i am taking new challenge.
What do you dislike about the product?
might by costly to none intensive ds teams
What problems is the product solving and how is that benefiting you?
Agriculture
A Must Have Aid For Precise Language Data Annotation
What do you like best about the product?
Cleanlab Studio’s big advantage lies in automating the finding of mislabeled data, a game-changer for our AI projects. It boasts an easy-to-use interface and strong algorithms that significantly reduce data cleaning time, thereby allowing our team to engage more on model development. What’s more, this has made it possible to improve workflows through seamless integration with existing data pipelines thus making maintenance of high-quality datasets easier.
What do you dislike about the product?
The main limitation is that there are no advanced customization options available for the purpose of cleaning up the data. In some cases where more detailed control would have been useful, although automated features can be very effective. Also, setting up initially may be somewhat complicated and requires some familiarization time with all these functionalities.
What problems is the product solving and how is that benefiting you?
Mislabeled data is a major concern addressed by Cleanlab Studio which affects performance of machine learning models severely. This enhances model precision by automating validation and correction processes of its datasets reducing manual data checks lead times. Thus we have seen shorter time frames for project completion and higher customer contentment resulting into success because it aligns perfectly with our agency’s need for speed and accuracy.
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