Combining the best of human annotation and machine learning to deliver training data up to 20 times faster at up to 50% lower cost.
First, you label a subset of your data with human annotators. Those labels are plugged into a model in the Figure Eight platform, which learns from those judgments. Then, you can leverage that model to annotate the rest of your unlabeled data.
Data rows that don’t reach the accuracy threshold you’ve set can be sent back to human labelers, while the rows that do reach your accuracy threshold are labeled automatically. Effectively, that means you’ll spend far less time–and money–getting labels from human annotators without sacrificing quality.
Figure Eight uses machine learning models from our most trusted partners. With these models, customers can quickly and easily prepare and label data for a wide variety of uses cases without having to build expensive custom models from scratch. The models will work with the classes and annotations that you choose for your unique business problem.
Combining ML and human-generated training data means saving time and money without sacrificing accuracy. That’s because models you train eliminates redundant or unnecessary labels from human annotators, only surfacing less confident rows to people and automatically labeling rows about which its confident.
Figure Eight Workflows automate multi-job processes through the UI using logic-based routing rules between models and jobs to generate aggregated results. Data uploaded to the workflow is routed between the model and a job creating a seamless process with little management by the user.
ML-powered labeling and workflows allow for the vast majority of your data to be labeled by a machine, even at high accuracy thresholds. The labelers you use for the rest are up to you. You can leverage dozens of our BPO partners, the 800,000 unique annotators in our network, or use your own internal resources for sensitive projects.
Interested in trying out our Machine Learning Assisted Data Labeling solution?
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