When ML teams send their data to companies like Scale.ai for labeling, most can only afford to label 1% or less of their datasets. But today they don’t have a good way to pick which 1% to label. We help them pick the best 1% of their data to label. By labeling the most representative data, they significantly improve model accuracy at the same cost.
Co-Founder at Lightly.ai. Previously worked at BCG, startups, and in finance. Studied at HSG, Harvard, and HEC Paris.
Igor has more than five years of experience in machine learning. He holds a degree in electrical engineering from ETH Zurich. During his studies, he developed a lot of experience in machine learning and robotics and had multiple successful publications in the area of deep learning at top conferences such as ICML and ECCV. He previously worked for two years at the Swiss stock exchange as a software engineer.