How to Use Machine Learning for Robust Data Quality Checks

Machine learning can be used to perform robust data quality checks, offering advantages such as scalability, adaptability to changes in data patterns, and the ability to detect unknown issues. However, it may not always be necessary or ideal for every scenario due to its complexity and overhead. When deploying ML, considerations include incorporating user feedback, evaluating model performance with appropriate metrics, addressing oversampling and imbalanced data, and utilizing open-source tools like Prophet or commercial offerings like Metaplane.