- The Three Edge Case Culprits: Bias, Variance, and Unpredictability - Apr 22, 2021.
Edge cases occur for three basic reasons: Bias – the ML system is too ‘simple’; Variance – the ML system is too ‘inexperienced’; Unpredictability – the ML system operates in an environment full of surprises. How do we recognize these edge cases situations, and what can we do about them?
- How Noisy Labels Impact Machine Learning Models - Apr 6, 2021.
Not all training data labeling errors have the same impact on the performance of the Machine Learning system. The structure of the labeling errors make a difference. Read iMerit’s latest blog to learn how to minimize the impact of labeling errors.
- Data Annotation: tooling & workflows latest trends - Mar 17, 2021.
As AI continues to boom, improved technologies and processes for data labeling and annotation are on the rise. iMerit, a leader in providing high-quality data for Machine Learning and AI, shares the latest trends in annotation workflow and tooling.