Training machine learning models for computer vision use cases takes massive amounts of images. Often, those images are mislabeled, broken or duplicated, leading to sub-par model performance. But with millions of images in many datasets, it’s virtually impossible to catch these issues. Visual Layer, a Tel Aviv-based startup that wants to enable data scientists and ML engineers to find these issues before they impact their models, today announced that it has raised a $7 million seed funding round led by Madrona and Insight Partners.
The company built a system that, without relying on expensive GPUs, can analyze hundreds of millions of images and automatically find potential issues within these data sets. At the core of Visual Layer’s technology stack is the open-source fastdub project. The company’s co-founders Read Entire Article
