AI and machine learning are enjoying widespread popularity. ML and AI applications in sectors such as healthcare, personalization, security, and customer support require that the data being used to train ML models is unbiased, balanced, error free and fit for purpose. It is crucial that quality of data is ensured and preserved throughout the intermediate steps of model training before machine learning models are put into production. Without a scientific process of data validation and verification the quality of data cannot be guaranteed. This talk will address process and methods for ensuring fit for purpose quality of data that are feeding AI and ML algorithms for a variety of applications.

Senior Specialist, Ph.D. Ahmed Khan Leghari, FORCE Technology