Machine learning’s impact on technology is significant, but it’s crucial to acknowledge the common issues of insufficient training and testing data.

Training data is a collection of examples that the model learns from to identify patterns and make predictions.

For those looking to get the most out of their AI system, synthetic data proves useful when real historical data is scarce, sensitive or difficult to obtain.

For AI developers and industry leaders, effectively leveraging public data can be the difference between breakthrough innovation and costly underperformance.

The Training Data Project wins the ICEAA Best Paper Award for pioneering work on data labeling, showing how AI success begins with accurate, cost-effective, and accountable training data pipelines.

Quality data is at the heart of the success of enterprise artificial intelligence (AI). And accordingly, it remains the main source of challenges for companies that want to apply machine learning ...