The training data used to teach machine learning or computer vision algorithms is the foundation of successful models, as its quality directly impacts performance and accuracy. High-quality training data guides the model's foundational knowledge, enabling it to identify patterns in new, unseen datasets. Human data scientists, annotators, and teams play a crucial role in transforming raw data into labeled data using tools like Encord, which automates data labeling with micro-models, reducing manual annotation time by 6x compared to traditional methods. These micro-models are specifically designed for annotation tasks, intentionally overfitting to identify specific features, but not suitable for general problems. By leveraging these technologies, organizations can create high-quality training datasets, scale their annotation workflows, and power their model performance with data-driven insights.