Components of AI Testing

Essential Components of AI Testing

An AI Testing strategy can and often includes popular reliability modalities such as model evaluation and traditional monitoring of summary statistics.

• Evaluation frameworks are an important tool in the development phase of AI application and can provide insights such as “based on X metric, model A is better than model B on this test set.” In development, the primary role of evaluations is to ensure that the application is performing at an acceptable level. After the app is deployed to production, online evaluations can be performed on production data to check how key performance metrics vary over time. Evaluation frameworks provide a set of off-the shelf metrics that you can use to perform evaluations, however many people choose to write custom evaluations related to business metrics or other objectives specific to their use case.

• Observability/monitoring tools log and can respond to events in production. By nature, observability tools catch issues after the fact, treating users as tests, and often miss subtle degradations. Most observability tools look at summary statistics (e.g. P90, P50, accuracy, F1-score, BLEU, ROUGE) over time, but do not provide insights on the underlying distributions of those metrics, and across time.

In an AI Testing framework, you can use eval metrics as the basis for tests by setting thresholds and an alert when that test, or some set of tests fails. With Distributional in particular, you can bring your own custom evals, eval metrics from an open source library, or use the off-the-shelf eval metrics provided by the platform.

Tests can also evaluate more abstract concepts such as whether or not your app looks similar to the day (or week) before.