In discussions about AI tools and workloads, the emphasis tends to be on optimizing machine learning models, rather than ensuring they are fed high-quality data. But, if garbage in means garbage out, then keeping data quality high is just as important as building and training the models.
In a guest article for Solutions Review, Dataddo CEO Petr Nemeth discusses four categories of tooling that can help businesses achieve AI-friendly data quality, with takeaways for each category:
- Data integration
- Data profiling and filtering
- Data labelling
- Data monitoring and lineage
Read the full article at solutionsreview.com.
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