"Despite an ample supply of data annotation service providers, their services cannot readily be applied in many industries and domains. Reasons are manifold, including privacy concerns, a requirement for deep domain expertise, or the literal absence of data samples for annotation. To address these challenges, a sprawling research field has developed a plethora of sparse annotation strategies, including active learning, unsupervised and semi-supervised learning, and approaches that rely on synthetic data. In this talk, we will discuss two projects that relied on these approaches. First, we will discuss training an adaptive 3d object detection model using synthetic data, for detecting rarely confiscated prohibited items in 3D CT X-ray images of passenger luggage. Second, we will discuss an application of active transfer learning to training a face recognition model for identifying individual chimpanzees in wild-life footage. In the context of these projects, will provide a brief overview of related research and other applications of these approaches. We will also highlight the specific technical and procedural challenges we faced and offer actionable insights and best practices.