As data volumes continue to grow exponentially, reaching an estimated 97 zettabytes globally in 2022 and projected to exceed 181 zettabytes by 2025, the fields of artificial intelligence (AI) and machine learning (ML) increasingly rely on effective data curation to extract meaningful insights. High-quality data curation is crucial for AI systems, particularly those utilizing computer vision (CV) algorithms, as these models typically process vast amounts of unstructured data such as images. The data curation process involves several steps, including data collection, validation, cleaning, normalization, de-identification, transformation, augmentation, sampling, and partitioning, to ensure datasets are accurate, relevant, and unbiased. In computer vision tasks, data annotation plays a pivotal role, requiring techniques such as bounding boxes, landmarking, and tracking to label images correctly for model training. Challenges such as evolving data landscapes, data security concerns, infrastructure scalability, and data scarcity in critical domains like healthcare highlight the need for robust data curation practices. Platforms like Encord offer comprehensive tools to streamline the curation process, improve data quality, and enhance model performance through features like automated workflows, vector embeddings, and active learning, underscoring the ongoing importance of data curation in the AI and ML landscape.