Customer Case Studies and Testimonials

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"Through this collaboration, SDSC and Lightly have combined their expertise to revolutionize the process of frame selection in surgical videos, making it more efficient and accurate than ever before to find the best subset of frames for labeling and model training."

“Lightly enabled us to improve our ML data pipeline in all regards: Selection, Efficiency, and Functionality. This allowed us to cut customer onboarding time by 50% while achieving better model performance.”

“Lightly gave us transparency to a part of the ML development that is a black box, data. Furthermore, Lightly enabled us to do Active Learning at scale and helped us improve recall and F1-score of our object detector by 32% and 10% compared to our previous data selection method. We finally saw the light in our data using Lightly.”

"Lightly is hyper-focused on finding thousands of relevant images from millions of video frames to improve deep learning models. The Lightly platform enabled us to build models and deploy features more than 2x faster and unlock completely new development workflows. I can recommend every MLOps team with a lot of data to integrate Lightly."

"I was truly amazed once we received the results of Lightly. We knew we had a lot of similar images due to our video feed but the results showed us how we can work more efficiently by selecting the right data"

"After training a model on the filtered data suggested by Lightly, I saw a dramatic increase in performance on our key metrics. Part of this is certainly because this was the first time we trained a model on any data that we've collected, but I'm fairly certain that performance would not have been as good if we had chosen what data to label at random."

"Lightly helped us understand more about our own data-gathering process. Through their service, we were able to see, that a lot of data being collected was not meaningful enough for training an accurate model. This led us to change the way we gathered data and allowed us to ultimately create a much more information-dense and higher-quality dataset overall. Needless to say, the performance of our final model was greatly improved."