In this session, you will learn more about:

• Uncover the necessity of ongoing assessment for improving model performance in machine learning
• Learn how data set attributes directly impact model outputs, revealing biases and flaws
• Find out why feedback loops must be established between dataset assessment and model performance for ongoing enhancements
• Explore practical examples showcasing iterative dataset analysis’ impact on actionable decisions
• Gain dataset insights for adjusting model architecture and data collection strategies