  The quality of training data has a huge impact on the efficiency, accuracy
and complexity of machine learning tasks. Various tools and techniques are
available that assess data quality with respect to general cleaning and
profiling checks. However these techniques are not applicable to detect data
issues in the context of machine learning tasks, like noisy labels, existence
of overlapping classes etc. We attempt to re-look at the data quality issues in
the context of building a machine learning pipeline and build a tool that can
detect, explain and remediate issues in the data, and systematically and
automatically capture all the changes applied to the data. We introduce the
Data Quality Toolkit for machine learning as a library of some key quality
metrics and relevant remediation techniques to analyze and enhance the
readiness of structured training datasets for machine learning projects. The
toolkit can reduce the turn-around times of data preparation pipelines and
streamline the data quality assessment process. Our toolkit is publicly
available via IBM API Hub [1] platform, any developer can assess the data
quality using the IBM's Data Quality for AI apis [2]. Detailed tutorials are
also available on IBM Learning Path [3].
