An unstructured data analytics platform - Deep learning service made easy and fast for all
The labeling time will be reduced 70-80% thanks to the auto-labeling technology by Brightics Deep Learning. In addition, costs and trial & error will be reduced as well.
The learning pace will be improved by providing recommended functions of learning models in eariler stages, which is optimized for learning data.
The pace of data process in bulk will be 8 times more improved by securing high-performance distributed processing, which proceeds with collection of data in bulk/pre-process/learning processes in distributed manners.
By doing deep learning analytics for ECG data collected through wearable devices, the data of sensitive and various types of arrhythmia can be accurately diagnosed. Hospitals and research institutes can utilize such analytics results for treatment and clinical research.
It is possible to check whether those with risks of hereditary cancers (e.g. family medical history) were actually born to have genotypic variation of cancers by thoroughly reviewing the list of targeted genotypic variation provided by Genomics in a clinical manner.
The duration for insurance payment process can be reduced and proper insurance premium can be calculated by providing proper repairing and insurance premium through pattern analysis of accident images in the case of processing car accidents.
The pace of data process in bulk will be 8 times more improved by securing the world's best high-performance distributed processing, which applies the most optimized load distributed methods to the company's own patented DB and the analytics server.
The time that requires on-site experts or less skilled AI developers to select deep learning models will be at least 2-8 times more improved by utilizing the pre-trained model that automatically recommends the most optimized deep learning models for the given learning data.
The labeling time will be reduced up to 80% by automatically carrying out the 80% of data labeling while human can implement the rest of 20% by maximizing the labeling automation rate with the most advanced active learning technology.
x86 server with 2 or more nodes
-GPU: NVIDIA V100 1EA/Node
-CPU: 16 cores/Node
-Operating system: Linux 64bits
- Browser : Chrome (Version 50.0 or higher)
- Screen resolution: 1280 x 900 (recommended)
SDS Cloud, Amazon Web Service, MS Azure, Google Cloud, etc.