Building effective computer vision (CV) models that function in real-world, variable environments, such as those encountered in AgTech and robotics, requires addressing several challenges, including inconsistent lighting, sensor instability, and biological variation. A key bottleneck in this process is the curation and labeling of edge-case data, which is crucial for creating models that are robust rather than just theoretically accurate. Encord offers solutions to streamline this process by enabling the efficient identification and annotation of high-impact edge cases using tools like visual search, metadata filters, and ML-powered embeddings. By focusing on edge-case sampling rather than sheer data volume, teams can enhance model performance and deployment, particularly on edge devices with limited resources. Successful CV systems are built through iterative processes that emphasize diversity in datasets, prioritize edge-case labeling, and ensure continuous feedback loops for model evaluation and retraining. Encord provides the infrastructure to support these strategies, helping teams build resilient CV pipelines that can handle the complexities of real-world applications.