12 Data Annotation Challenges for Retailers (And How to Overcome Them)
Make data your competitive advantage with answers to these frequently asked data labeling questions.
At Sama, we’ve helped hundreds of organizations overcome data challenges at every stage of the ML model lifecycle. A lot of the same questions come up, and we’ve compiled them here along with recommendations for approaching your data annotation strategy holistically and sustainably.
Jerome Pasquero, Product Manager at Sama, provides answers to burning data labeling questions such as:
- How do I collect and store my training data?
- How much data do I need, and when?
- Which parts of my training data should I get annotated first?
- How do I capture edge cases and deal with complexity?
- How negatively impactful are errors in my data?
- Who can I trust to annotate my training data?
About the author
Jerome Pasquero holds a Ph.D. in electrical engineering from McGill University and has gone on to build leading-edge technologies that range from pure software applications to electromechanical devices. He has been a key contributor to the design of innovative and successful consumer products that have shipped to millions of users. Jerome is listed as an inventor on more than 120 US patents and has published over 10 peer-reviewed journal and conference articles. For the past 5 years, Jerome has been leading a number of AI product initiatives.