Objective of this Webinar was to provide complete understanding of Artificial Intelligence(AI) and Machine Learning(ML). AI & ML are known as pervasive technology and both used in business and personal life. All the models used in machine learning are data-driven, automatically learning patterns and information from the data. Therefore, a machine learning model is only good as the data that is fed to it. While researchers and practitioners have focused on improving the quality of the model such as neural architecture search and automated feature selection, there is a limited amount of effort towards improving the data quality for machine learning.

In this Session, Ms. Hima Patel, Research Manager, IBM Research discussed on Data quality for AI, Low quality data versus high quality data for AI, how can data quality be measured and what are some of the challenges faced with data quality for AI. Ms. Patel has also focused on specific example of label noise challenge and how this can be avoided.

Session Speaker has also answered the questions after the completion of session.

It was overall good learning experience for all the participants.