EVERWIN GLOBAL LANGUAGE
The Leading Globalization Services Provider
SMART LABELING
Machine learning assistance to accelerate ROI on your AI initiatives
Our Smart Labeling capabilities have built-in Machine Learning assistance to save customers time, effort and money - delivering high-quality training data and accelerating the ROI and your AI initiatives.
Ensuring you have enough labeled data to adequately train your models is no easy feat. Each model may need hundreds of thousands, if not millions of rows of labeled training data from which to lean. Collecting and annotating that amount of high-quality data consumes a lot of time and resources - especially if you’re relying on the power of human annotations alone.
That’s why our industry-leading data annotation platform has Smart Labeling. This suite of innovative capabilities uses Machine Learning assistance in the data annotation process to automate and improve productivity, quality, and delivery of your data collection and data annotation projects.
EGL SMART LABELING FOCUSES
ON THREE SPECIFIC AREAS
Machine Learning can drive quality, cost and time-saving in the data annotation process:
PRE-LABELING
Machine Learning provides an initial 'best guess' hypothesis before contributors start the task. With human contributors reviewing pre-processed annotations instead of starting a judgment from scratch, the time needed to annotate data drastically reduces.
SPEED LABELING
Machine Learning provides for in-tool efficiency, quality, and accuracy, improving ergonomic conditions while contributors work. This reduces cognitive strain and allows contributors to work faster and more comfortably, increasing the throughput of their annotations.
SMART VALIDATORS
Our Machine Learning models verify human judgments before they are finalized. This ensures you are only paying for quality judgments, eliminating the need for peer reviews and the risk you’re paying for judgments that don’t fit your requirements.
WHAT WE DELIVER
PRE-LABELING FOR AUTONOMOUS VEHICLE IMAGE PIXEL LABELING
Images are automatically applied with Pixel Labeled classes based on the ontologies you choose. They are then reviewed by a contributor to ensure the annotations are correct and to your specifications. Our statistically significant A/B tests show Pre-Labeling PLSS can improve contributor productivity by 91.5% without quality compromise.