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Heartex Label Studio empowers Data Science teams to manage large-scale data labeling for any type of data. Labeling teams large, small, internal, and external produce better labels and more accurate models with Heartex.
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Label Studio Community Edition, Heartex’s popular open source project, is ideal for small teams. Over 20,000 Data Scientists use Label Studio Community Edition to consistently generate high-quality labels and deliver accurate predictions in production.
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Label Studio Enterprise Edition, a powerful extension of Community Edition, helps teams scale labeling operations. Data Science teams rely on Heartex to manage internal and external annotators, optimize workflows, collaborate on labeling tasks, and manage quality with built-in analytics.
Label Studio Community Edition, Heartex’s popular open source project, is ideal for small teams. Over 20,000 Data Scientists use Label Studio Community Edition to consistently generate high-quality labels and deliver accurate predictions in production.
Label Studio Enterprise Edition extends Community Edition with powerful capabilities to scale labeling operations. Data Science teams rely on Heartex to automate workflows, collaborate on labeling tasks, ensure labeling quality, and manage labeling teams.
Data is everywhere and in every format. That doesn’t mean you need a different labeling solution for each data type. You only need Heartex Label Studio. Whether you’re labeling text, images, audio, or a little bit of everything, Label Studio has you covered.
People make mistakes. Collaborative data labeling, when multiple annotators label the same data, significantly reduces the number of inaccurate labels making their way into your training set and negatively influencing your model.
Managing all of those annotators might seem daunting.
Luckily, Label Studio simplifies the onboarding, management, and quality assessment of large (and small) data labeling teams. Whether you outsource your data labeling to a third party, manage it all in-house, or use a hybrid model, you need to be in control. Label Studio gives you the tools you need to organize and manage your data labeling team at scale.
People make mistakes. Collaborative data labeling, when multiple annotators label the same data, significantly reduces the number of inaccurate labels making their way into your training set and negatively influencing your model. Managing all of those annotators might seem daunting. Luckily, Label Studio simplifies the onboarding, management, and quality assessment of large (and small) data labeling teams. Whether you outsource your data labeling to a third party, manage it all in-house, or use a hybrid model, you need to be in control. Label Studio gives you the tools you need to organize and manage your data labeling team at scale.
Create Label Studio projects to organize and manage a set of data labeling tasks
Organize annotators into Label Studio teams and assign teams to specific projects
Define workflows and how data labeling tasks are assigned to annotators
Monitor annotator and team throughput and quality performance
Easily invite new annotators to your account, teams, and projects and assign them roles and permissions
Role Based Access Controls →
Managing successful, high-quality data labeling projects requires real-time visibility into annotator performance and label quality. Label Studio’s built-in quality management and analytics surfaces label quality issues.
Label Studio automatically surfaces discrepancies in collaborator labeling so you can quickly fix label issues, manage annotator performance, and ensure accurate labeling before training.
Label Studio’s Data Manager organizes your dataset into an easy to digest format including annotations and key metrics such as collaborator agreement and prediction scores.
Easily monitor individual annotator and team productivity. See how many tasks are completed, how many have been skipped, and the predictive performance for the team and the individual.
Most Data Scientist’s consider data labeling an activity you do before your model is in production. While that is true, it isn’t the whole truth. The best way to improve your models is to analyze the results from production, label that data, and use it to retrain the model.
You can do all of that with Label Studio. Leverage the same solution, with the same UI, workflows, and quality management whether you’re labeling for a pre-production model or fine-tuning a model already serving predictions in production.
With secure servers and limited outsite operations, we ensure your data is not compromies
