The term quality metrics for training data is particularly important in the fields of Artificial Intelligence, Big Data and Smart Data, as well as Digital Transformation. Training data is the foundation with which computer programs, for example for speech or image recognition, \u201elearn\u201c. For Artificial Intelligence to work reliably, this training data must be of good quality.<\/p>\n
Quality metrics are indicators that check exactly that. They help to determine how complete, accurate, and relevant the data is before it is fed into a system. This prevents erroneous or biased data from leading to poor results or wrong decisions.<\/p>\n
A clear example: A company wants to develop an AI system that automatically sorts emails. For the programme to do this reliably, quality metrics for training data check whether all emails are correctly labelled, no important emails are missing, and no duplicate emails are present in the dataset. Only with good training data does the system learn correctly \u2013 this improves automation and saves time and costs. This is how quality metrics for training data help to make artificial intelligence smarter and more reliable.<\/p>","protected":false},"excerpt":{"rendered":"