Machine learning algorithms require large datasets to improve performance and produce accurate results. High-quality datasets are essential to ensure the best possible outcomes from artificial intelligence projects. Utilizing open-source datasets is a great way to obtain high-quality data, with hundreds of free and large-volume options available. To train machine learning models effectively, it's crucial to align the dataset with project goals, verify annotation quality, and assess image/video conditions. A well-trained model can only be achieved by providing sufficient examples of objects that contrast with the target object(s) in question. Assessing performance is essential, as failure is a natural part of computer vision projects. Failure rates are expected to be high initially, but using this data to create a feedback loop can help identify areas for improvement. If more data is needed, synthetic data creation or purchasing datasets from proprietary sources may be necessary. Retraining the model and reassessing performance until desired standards are achieved is crucial to ensure accuracy and success.