Workflows is a simple platform interface that allows our users to easily build and automate multi-step data annotation projects, so they can create high-quality training data sets faster and easier than ever before.
Figure Eight Federal’s Workflows interface allows customers to build and automate multi-step data annotation projects with ease. The simple graphical interface ensures that no coding skills are required to create even complex workflows consisting of multiple jobs.
Our customers now have access to this powerful data pipeline solution that reduces job latency and increases efficiency via automated data routing. Users can break down complex data annotation projects into smaller, easier tasks, then enjoy greater control over customization of job designs and annotator channels at each step.
Additionally, users can add machine learning models to their workflows with Machine Learning Assisted Data Labeling (MLADL) capabilities to further streamline their data pipeline. Our human-machine-teams then harness predictions from MLADL models to deliver to our customers large quantities of annotated data up to 20 times faster, at up to 50% lower costs.
Workflows uses an intuitive graphical user interface (GUI) to enable any user, regardless of user skill, to configure sophisticated data operations using easily understood routing rules, eliminating their reliance on additional technical support.
Users can easily automate data routing and the aggregation of results into a single job report, eliminating the latency caused by manual job creation for individual jobs that would normally be run sequentially.
Users can easily break jobs into smaller tasks with no coding necessary, and complete jobs faster by automating the data routing between data operations. This additionally reduces the rate of human error. Users can also optimize the quality of their annotation results using the ability to customize contributor channels or target specific annotators for every task.
Users can leverage our powerful human-machine-teaming methodology to deliver large volumes of annotated data up to 20 times faster at up to 50% lower cost, allowing them to scale their AI initiatives to new heights.
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