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Now Machine Learning Can Label Data Too
A new desktop labeling application that allows developers, engineers, and data scientists complete data ownership, privacy, and control, along with swift labeling of data using ML.
FREMONT, CA: For 600 years, people used waxed ribbons to fasten pieces of papers together. The routine was messy and would tamper the paper, meanwhile affecting the information written on it. It was then a paper clip was invented. Comprising of two small hoops made out of wire that held the sheets of paper together, the minuscule object ensured that the data written on it was intact. Regardless of how simple or complex the thought behind making the paper was, it was a significant invention. Similarly, in the digital age at present where data is the currency, it is essential for the saved data to be structured. An application which pins data alike, and pins the different-unstructured data into a different bundle is as significant as an earlier invention.
One such company who has worked on creating this momentous application is Sixgill, LLC. The company announced its new offering, the HyperLabel, a desktop data labeling application for Machine Learning (ML) models. The app provides the fastest method to creation of high-quality labeled datasets for enriched ML models.
External services for the same can be avoided with HyperLabel. Users can obtain complete ownership, privacy, and control of the data the company possess while accelerating the project onboarding and completion speedily. It’s a desktop app, with all cloud-free, locally installed and highly scalable.
HyperLabel is designed to be quick, simple, and accurate, from the installation to label exporting. The app provides straightforward customizations and explanations that are clear. ML technology is utilized by the app itself to enable efficient labeling projects with speed and accuracy boost. The ML models are pre-trained to carry out standard routines and will automatically create labels for the data.
Effective batch reviews of labeled data are enabled with an easy QA interface to deepen the streamlining and simplification of the process. The application cuts down the extra time developers, data scientists, and engineers spend on data labeling and allows more time to train the ML models.
HyperLabel upends on the assumptions that accurate labeling is slow and inevitably meticulous. Especially now, with the applications, there will be no necessity to punch holes and wax ribbons.