To visualize AI model performance metrics effectively, which two methods should be used?

The use of a line chart for visualizing accuracy and loss over time is particularly effective in understanding how these metrics evolve throughout the training process. Line charts enable viewers to easily observe trends, patterns, and fluctuations in both accuracy and loss, which helps in diagnosing the model's learning behavior over the epochs. Additionally, a heatmap for resource utilization provides a straightforward way to visualize the distribution of resources such as CPU and GPU usage, memory consumption, or other performance metrics across different runs or configurations. This dual-approach of employing a line chart and a heatmap captures both the model's performance metrics and the underlying resource usage in a clear, interpretable manner.

Other options such as using pie charts or histograms compromise on clarity or analytical depth regarding model performance over time, making them less suitable choices for effective visualization.