Assets

Our auditing technology assists in addressing an often missed but crucial step in AI development. The technology provides an easy to use interface that puts the most important aggregate metrics front and center while also empowering a deep dive into random samples, both graphically and tabularly.

Auditing training and test data before commencing model training is a critical step in ensuring the integrity and quality of the machine learning process. Thorough auditing allows users to identify and address potential issues that could adversely affect model performance. By carefully examining the data, users can ensure that it is clean, accurate, and representative of the problem domain, which is essential for building robust and reliable models.

Additionally, auditing helps verify that the training and test datasets are properly aligned and that the test data remains a valid proxy for evaluating model performance. This preliminary review not only enhances the model’s ability to generalize to new, unseen data but also contributes to more trustworthy and actionable insights.

Overall, a meticulous audit of both training and test data lays the foundation for a successful machine learning project, helping to prevent issues that could undermine the validity of the model’s results and its practical applicability.