KPI Examples

15+ Artificial Intelligence KPIs

AI Compliance Score score

Assesses the AI model's adherence to ethical guidelines, regulatory standards, and best practices in AI development.

Data Completeness %

Data Completeness evaluates the extent to which necessary data is available for model training.

Data Diversity Index score

Measures the diversity in the training dataset, ensuring that the model is exposed to a wide range of scenarios.

Data Pipeline Processing Time time

This KPI tracks the time taken for data to move through the entire pipeline, from collection and processing to being ready for use in model training.

Data Throughput ratio

Measures the amount of data processed per unit of time in the data pipeline, indicating the pipeline's efficiency and capacity.

Feature Importance Score score

Feature Importance Score evaluates the impact of different input features on the model’s predictions.

Label Accuracy %

Label Accuracy quantifies the correctness of the labels in the training dataset.

Model Accuracy Rate %

This metric assesses the overall accuracy of an AI model, indicating the percentage of total predictions made correctly, both positives and negatives.

Model F1 Score score

The F1 Score is the harmonic mean of Precision and Recall, providing a balance between them.

Model Failure Rate %

The frequency at which the AI model fails to provide a valid output or encounters errors during operation.

Model Precision %

Model Precision measures the accuracy of positive predictions made by an AI model.

Model Recall %

Model Recall, or Sensitivity, calculates the proportion of actual positives correctly identified.

Model Robustness Score score

Model Robustness Score measures an AI model's ability to maintain performance when exposed to new, unseen data or adversarial conditions.

Model Scalability Rate ratio

Evaluates how well an AI model maintains its performance as the amount of data increases.