Ontologies are crucial for solving computer vision challenges by providing structured data organization, consistent labeling, and necessary information for models to perform effectively. In Encord, users can create complex and nested ontologies with features like Dynamic Classification for real-time video annotation adaptability. Ontologies improve data quality and reduce errors by adhering to predefined concepts, relationships, properties, and attributes. They include objects and classifications that assist in precise annotation tasks such as semantic segmentation and pose estimation. Encord's ontology features enhance machine learning projects' scalability and granularity, which are particularly beneficial in complex tasks like medical diagnosis and sports analytics. In these fields, ontologies elevate model performance, accuracy, and interpretability by capturing intricate relationships and enabling rapid experimentation and innovation. The use of Encord's tools allows for the development of robust, insightful results in machine learning applications, as demonstrated in real-world case studies from the medical and sports sectors.