ML models are only as good as the datasets they are trained on. The quality of the dataset plays a pivotal role in determining the performance and reliability of the resulting models. However, achieving high-quality datasets is not a one-time task; it requires an iterative process of understanding, assessing and refining data to enhance model performance continually. Johansson delves into the critical role of iteratively assessing dataset quality concerning model performance in the realm of machine learning, focusing on ADAS/AD use cases.

• 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