v5.0
Various utilization options for on-site MLOps implementation
MLOps architecture design suitable for any on-site environment.
Build field optimized retraining system with API/CLI training engine
Build field optimized retraining system with API/CLI training engine
Build field optimized retraining system with API/CLI training engine
Generate multiple models simultaneouslywith full GPU utilization.
Generate multiple models simultaneouslywith full GPU utilization.


Automatically generate recommended label areas from a small set of labeled data with Auto Labeling. Now supporting GPU.

Automatically generate recommended label areas from a small set of labeled data with Auto Labeling. Now supporting GPU.

Generate multiple synthetic defects with multiple target areas specified

Generate multiple synthetic defects with multiple target areas specified


Faster Runtime Inference Speed through Data Pipeline Improvements
Faster Runtime Inference Speed through Data Pipeline Improvements
Prevent productivity loss and inspection bottlenecks caused by on-site inference delays by setting the maximum time allowed for inference
Prevent productivity loss and inspection bottlenecks caused by on-site inference delays by setting the maximum time allowed for inference





7F, 30, Godeokbizvalley-ro 4-gil, Gangdong-gu, Seoul, 05203, Korea
+82-2-6952-6897
info@neuro-cle.com
+82-2-6952-6898
neurocle@neuro-cle.com


7F, 30, Godeokbizvalley-ro 4-gil, Gangdong-gu, Seoul, 05203, Korea
+82-2-6952-6897
info@neuro-cle.com
+82-2-6952-6898
neurocle@neuro-cle.com
