Lightly AI addresses a critical challenge in computer vision - improving model performance through better training data rather than just algorithm tweaks. Its platform analyzes image datasets to identify label errors, redundant samples, and underrepresented classes that cause bias. ML engineers use Lightly's active learning capabilities to prioritize the most valuable new images for annotation, reducing labeling costs by up to 80%. Key features include similarity clustering, drift detection between training and production data, and synthetic data recommendations. Autonomous vehicle teams apply it to sensor data curation, while e-commerce companies optimize product recognition models. The solution integrates with popular ML pipelines (PyTorch, TensorFlow) and annotation tools (Labelbox, CVAT), becoming particularly valuable as datasets scale beyond millions of images.