In this session we will focus on the critical role that high-quality data plays in the effectiveness and accuracy of AI models. Since AI systems learn patterns from data, ensuring that the data is clean, diverse, accurately labeled and regularly updated is essential for optimal performance. Poor-quality data can lead to inaccurate predictions, biased results and underperforming models. By implementing strategies such as data cleansing, augmentation and proper annotation, organizations can improve the training process, resulting in more reliable, fair and effective AI systems. The success of AI initiatives depends as much on the data used as on the algorithms themselves. Join us to learn how to optimize the use of your data.