How to sharpen machine learning with smarter management of edge cases

Machine learning (ML) applications are transforming business strategy, popping up in every vertical and niche to convert huge datasets into valuable predictions that guide executives to make better business decisions, seize opportunities, and spot and mitigate risks. While ML models are rife with potential, it's quality data that allows them to become accurate and effective. Today's enterprises are handling huge floods of data, including unstructured data, all of which needs annotating before ML models can produce dependable predictions. Data processing is often under-scrutinised, but it's crucial for accurate and relevant forecasts. If data is mislabeled or annotated incorrectly, all your predictions will be based on misconceptions, making them basically untrustworthy.