Data Digest: Data Quality and AI/ML
Why AI needs high-quality data, techniques for improving data quality, and ideas for incorporating ML in the workplace.
- By Upside Staff
- October 24, 2023
The accuracy and reliability of AI outputs are intrinsically linked to the quality of the underlying data.
Read more at Data Science Central
Data integration, data profiling and filtering, data set labeling, and data monitoring and lineage are essential for improving data quality in AI initiatives.
Read more at Solutions Review
This article provides business leaders with evidence-based strategies for successfully integrating machine learning into the workplace, focusing on augmenting human decision-making rather than replacing it.
Read more at Forbes