The Problem with Annotation
As data grows, so does the need for labels. While fully manual data annotation is time-consuming and prone to human error, fully AI-automated annotation often inaccurately labels significant portions of the data.
The Solution: Cleanlab Auto-Labeling Agent
Auto-Labeling Agent is a human-in-the-loop solution that blends human insight with AI-assisted labeling speed. Teams only label a bit of data, to let our AI learn from this supervision and then auto-label the data it confidently can. Iterating these steps reduces manual effort by 80%, mitigates automation biases, and delivers high-quality annotations cost-effectively. Without writing a single line of code, you can experience seamless data annotation with Auto-Labeling Agent.
Why Choose Auto-Labeling Agent?
These key features set our Auto-Labeling Agent apart to provide you the simplest and powerful workflow on the market.
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AI-Powered Precision Our Auto-Labeling Agent leverages in-house advanced AI algorithms to automatically label your data with exceptional accuracy. Experience the power of Foundation models with a massive amount of context while our AI trains iteratively to learn domain-specific patterns in your dataset.
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Confidence Scores for Label Suggestions Our AI provides calibrated confidence estimates that help you quickly determine what subset of the data it can accurately label. For some datasets, this can be millions of data points you no longer need to worry about.
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Significant Time and Cost Savings Exponentially reduce how much time your team spends on data annotation, or your outsourced data labeling labor costs. Letting our AI handle the portion of the data that can be confidently auto-labeled results in massive savings for your team.
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Label the most informative data first (Active Learning) Auto-labeling Agent further reduces effort, by helping your team focus manual labeling effort on only the most informative data points. Labeling these particularly informative data points improves the AI system more than other examples would, and the product automatically presents them to you. Label a few of these informative data points, and then (with one click) retrain the AI system to more confidently and accurately auto-label additional data. Iterating these steps in multiple rounds is the lowest effort way to get any dataset labeled with high accuracy.
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Automated label quality assurance Our AI also auto-detects labeling errors made during this process, to save you annotation reviewing efforts. This is done via state-of-the-art confident learning algorithms.
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Native integration with Cleanlab Studio Auto-labeling Agent is a powerful feature of the Cleanlab Studio platform for Data-Centric AI. Simply load your mostly unlabeled dataset and a wizard will walk you through the auto-labeling process. Beyond data labeling, Cleanlab Studio can also auto-detect all sorts of common issues in your data. You can also directly train and deploy state-of-the-art AutoML to make predict the labels of new data – all without writing any code.
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Data Agnostic Flexibility Whether you’re working with images, text or video, our agent is designed to handle it all. Its data agnostic capabilities ensure that no matter the format, you receive precise and consistent labels, ready for your next breakthrough.
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Scalability Whether you’re a startup or a large enterprise, our solution scales with your needs. Handle small or massive datasets with equal ease, ensuring that your growth is never hindered by annotation bottlenecks.
How it Works
Simply import a dataset to Cleanlab Studio with a few examples labeled for each class (we recommend at least 5 per class). When you create a Create a Project in the platform, the Auto-Labeling Agent will spin up automatically whenever your dataset is under 50% labeled. With one click, you can have our AI auto-label a large fraction of your dataset, while also reviewing data/label issues that Cleanlab Studio detected in your dataset.
Watch how we confidently auto-label 1326 examples in a news article dataset, as shown in the tutorial video below:
After specifying the data to auto-label, you will be presented with the most informative examples in the remaining unlabeled dataset for you to optionally label a few, before the AI is automatically re-trained to suggest labels more accurately/confidently. To label all of the data quickly and accurately, you can iteratively repeat this process of: auto-labeling examples that are confidently handled by our AI, manually labeling a few informative examples, and re-training our AI with the freshly labeled data.
Tutorials and Resources
- Get started with this no-code video walkthrough.
- Learn more details via our data labeling tutorial.
- To get auto-labeling and label quality assurance without changing your existing annotation workflows, check out our tutorial on running Cleanlab Studio on top of any existing data annotation platform.
Transform your annotation process today
It’s free to try, with no credit card required.
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