Cleanlab develops a data-centric artificial intelligence (DCAI) to help companies improve the quality of their datasets. It handles the entire data quality and data-centric AI pipeline in a single framework for analytics and machine learning tasks.

Cleanlab

Pioneered at MIT and proven at Fortune 500 companies, Cleanlab provides the world's most popular Data-Centric AI software.

Most AI and Analytics are impaired by data issues (data entry errors, mislabeling, outliers, ambiguity, near duplicates, data drift, low-quality or unsafe content, etc); Cleanlab software helps you automatically fix them in any image/text/tabular dataset. This no-code platform can also auto-label big datasets and provide robust machine learning predictions (via models auto-trained on auto-corrected data).

What can I get from Cleanlab software?

1. Automated validation of your data sources (quality assurance for your data team). Your company's data is your competitive advantage, don't let noise dilute its value.
2. Better version of your dataset. Use the cleaned dataset produced by Cleanlab in place of your original dataset to get more reliable ML/Analytics (without any change in your existing code).
3. Better ML deployment (reduced time to deployment & more reliable predictions). Let Cleanlab automatically handle the full ML stack for you! With just a few clicks, deploy more accurate models than fine-tuned OpenAI LLMs for text data and the state-of-art for tabular/image data.

Turn raw data into reliable AI & Analytics, without all the manual data prep work.

Most of our cutting-edge research powering Cleanlab tools is published for transparency and scientific advancement:  cleanlab.ai/research/

Lately we've seen many companies like Berkeley Research Group  improving the accuracy of their ML models on the order of 15% using Cleanlab Studio (which is currently still in beta: https://lnkd.in/gmhHyipZ).  Cleanlab Studio is getting close to general availability, so everybody can easily achieve these gains soon!  Use Cleanlab to fix your dataset and you don't have to change anything about your model to see its performance go way up 🔥🔥 

#datacentricai  #machinelearning #datascience

In a new hands-on tutorial, Matt Turk shows how you can featurize time-series data into a standard tabular format to use in a supervised learning model.

🎉  Big news for open-source practitioners of Data-Centric AI:

We just released major updates to cleanlab, the most popular software library for Data-Centric AI (with 8000 GitHub stars thanks to an amazing community).

Flawed data produces flawed AI, and real-world datasets have many flaws that are hard to catch manually.

With one line of Python code, you can run cleanlab on any dataset to automatically catch these flaws, and thus improve almost any ML model fit to this data. Try it quickly to see why thousands of engineers have adopted cleanlab’s AI-based data quality algorithms to deploy more reliable ML.

Today’s v2.6.0 release includes new capabilities like Data Valuation (via Data Shapely), detection of Underperforming Data Slices/Groups, and lots more. We published a blogpost outlining new automated techniques this library provides to systematically increase the value your existing data:

Interesting application of our open-source CleanVision library 👀 

https://lnkd.in/gdPnwX5W

CleanVision is a lightweight Python package to automatically catch common problems in any image dataset.  We just released the newest version (v0.3.6) with big improvements to the algorithms used to detect images that are blurry or odd-sized.

Start practicing data-centric computer vision and systematically audit your datasets before diving into modeling

📢 Announcing a game-changer for: E-Commerce & Retail businesses

When growing beyond the thousands, product catalogs start developing all sorts of issues which become hard to catch (miscategorized products, near duplicates, low-quality images/descriptions, unsafe content, …). Whether or not your team is aware of these issues, they negatively affect customer experience (search, engagement, informed purchase decisions) and revenue.

Introducing an AI solution that provides comprehensive quality assurance for any product/content catalog, by automatically catching problems in the product images, text descriptions/reviews, and structured information (category, tags, brand, price, rating, size, …).

You’ve never seen any AI or Data Quality tool like this! 

It handles multi-modal data, understands the information in each data point to flag inaccuracies, and detects low-quality + unsafe content — all with ZERO configuration or manual rules.

Your customers will be delighted, and think your team worked super hard to clean up all the problems lurking in your catalog 😄

Read more in our new blog:

Is your team on Snowflake and excited about the many LLM offerings becoming available?  You’ll find that unlike quick demos, producing production-ready LLMs requires extensive data curation.

Flawed data produces flawed AI models, and flawless data is rare.

Today, we’re excited to announce support for Snowflake datasets in Cleanlab Studio, the most automated and easy-to-use data curation platform.  Use Cleanlab to automatically find and fix flaws in all of your structured & unstructured Snowflake data, and all of your AI projects will benefit.

See how this was done for a Snowflake table storing text to improve OpenAI LLMs by 37%:  https://lnkd.in/ee_3fCxU

Have you used tools for Data Quality/Cleaning, Data Annotation, AI/ML Modeling, and wondered how our Automated Data Curation platform differs?

Folks who haven’t tried it yet often ask because:
1. ALL of the above are handled in a single unified platform.
2. A Data-Centric AI platform like this has never before existed.

Our newest article provides detailed comparisons 👇
https://lnkd.in/g4VYFJ-x

Learn how much easier your Data Science and AI projects could be using a next-gen Data-Centric AI platform ✨ 

TLDR: We redesigned the entire Data Science & AI stack to create the fastest path from data to value. AI/ML modeling and Data prep (quality/cleaning or annotation) workflows are embedded together in a seamless flywheel. The AI models are used to find and fix issues in your data, which in turn improves the models. This is all automated, such that a lone data scientist can do: data annotation, data cleaning, ML training+deployment, all within hours for a big dataset.

We are excited to share the fastest way to curate clean training data for LLMs during fine-tuning for Q/A (instruction tuning) tasks.

Instruction Tuning (aka LLM Alignment / Fine-Tuning) datasets inevitably have bad data lurking within them. This is often what’s preventing your LLM to go from demo to production, not more parameters/GPUs… However, bad instruction-response data is hard to detect manually.

Our newest article (written by engineers, not marketers) demonstrates automated techniques to catch low-quality data in any instruction tuning dataset.

Applying our techniques to the famous dolly-15k dataset immediately reveals all sorts of issues in this dataset (even though it was carefully curated by over 5000 employees): responses that are inaccurate, unhelpful, or poorly written, incomplete/vague instructions, and other sorts of bad language (toxic, PII, …)

Data auto-detected to be bad can be filtered from the dataset or manually corrected. This is the fastest way to improve the quality of your existing instruction tuning data and your LLMs! 

Read our article to learn more:

Most legal teams aim to become more data-driven in 2024, but are challenged by a lack of clean data.  Nobody understands this more clearly than technical consulting teams hired to ensure success in Data/AI projects.

Leading consulting firm Deloitte published key strategies to keep data clean:

https://lnkd.in/gqzt3qTJ

The final key strategy outlined in their article is proper Data Curation, "including AI and advanced analytics as a curation tool".

Cleanlab's Data-Centric AI platform was pioneered precisely for this purpose! Many technical consultants and legal teams have used Cleanlab's AI to  automate their data curation with immense ROI (better results in 3x less time/costs).  We've repeatedly observed how this data curation critically enables projects to graduate from demo to deployed solution (across all sorts of applications, it often boils down to fixing edge-cases/problems in the data).

🗑️ in -> 🗑️ out


Guess what, and I hope this does not come as a surprise: An AI Strategy alone does not set your organization in a better position to embrace AI, if it is built on neglected data assets.

A couple thoughts here:

🛣️ AI Strategy should be part of a wider Data Strategy, build on a solid data culture, all in relation to your Business Strategy. If you think this is a lot of strategies, you are right: ideally Data & AI should be part of your Business Strategy, the only strategy in your organization. But this is really dependent on your data maturity.

🔧 Do not think of AI as only a tool. This is ignoring its complexity and value proposition. In your strategy, you need to define AI as a set of technologies and methods, relevant to your business.

✍️ An AI strategy should encompass a clear approach to how we think about Sourcing data. How should Data be collected, cleaned, curated for AI models, while ensuring secure external data sharing and integration into core business operations?

🐾 Without going into #explainableAI, there are options to trace data used in AI models, creating lineage, Watermarking data assets,.. You want to know, what data lead to what results.

⚙️ It comes down to #DataQuality. The quality of your data assets defines the value of your AI outcome. You still need to grind.

🤖 Data Professionals need to embrace AI in their own work in #DataManagement. Think of your challenges with #DataGovernance, Data Quality, #Metadata,... What can be automated? How can we ease our processes. Data professionals need to walk the talk.

👐 If you want your users to embrace new tech and methods, like AI, you need to be open about your Data Quality. Let's talk about known DQ, not good or bad, and make it available. This is about transparency and managing expectations.

I still think #AIStrategy is vital for organizations to really understand what value they can gain from AI and why AI makes sense in your very own setting.
But at the same time it just emphasizes the importance of Data Management.

Ole Olesen-Bagneux Anthony Alcaraz Achillefs Tsitsonis Charlotte Ledoux Karin Håkansson Sami Laine

Data Management Association Norway (DAMA)
MetaDAMA

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