In the development of commercial applications using machine learning, particularly in computer vision, the quality and selection of training data are critical for model performance. This involves ensuring data quality, relevance, and quantity, as well as maintaining high label quality to avoid errors like overfitting or poor predictions. Effective data curation and annotation are vital, and factors such as problem definition, data diversity, and available resources should guide the process. Tools like Encord Active can assist in managing data quality, annotation, and error detection, ensuring datasets are well-prepared for training robust AI models. Post-curation, it's important to create a baseline model to assess performance and potentially apply feature extraction to optimize learning. The use of open-source tools and pre-trained models is recommended to streamline data selection and enhance model effectiveness while minimizing the need for large datasets.