VentureBeat October 29, 2023
If there’s one thing that has fueled the rapid progress of AI and machine learning (ML), it’s data. Without high-quality labeled datasets, modern supervised learning systems simply wouldn’t be able to perform.
But using the right data for your model isn’t as simple as gathering random information and pressing “run.” There are several underlying factors that can significantly impact the quality and accuracy of an ML model.
If not done right, the labor intensive task of data labeling can result in bias and poor performance. The use of augmented or synthetic data may amplify existing biases or distort reality, and automated labeling techniques might increase the need for quality assurance.
Let’s explore the importance of quality labeled data in...
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