The biggest hurdle to clear when developing machine learning solutions has always been the data. Large-scale, clean, fully-annotated data sets like ImageNet and COCO are not easy to come by, especially for niche tasks. This is especially true of deep learning and will become more true as deep learning is increasingly adopted. To overcome the labeled data bottleneck, researchers and developers are developing a variety of techniques like transfer learning, domain adaptation, synthetic data generation, and many semi- and self-supervised techniques. The world of semi- and self-supervised techniques is a particularly fascinating field as it seems almost like magic.