AI-Specific Testing Procedures
Testing AI-based systems requires specific techniques and methods. In addition to the classical testing stages, AI-specific procedures are added: input data tests and ML model tests.
The input data test ensures the quality of the data used by the system and includes techniques such as reviews, static techniques, exploratory data analysis of the training data, as well as static and dynamic tests of the data pipeline. The ML model test validates selected models against the fulfillment of all established functional and non-functional performance criteria. If these criteria are not met, adjustments such as optimizing hyperparameters or changing the algorithm can be made.
Guaranteed quality for your AI solutions is not a coincidence. It requires a holistic prevention approach at every testing stage. Let us work together to ensure the reliability of your AI-based systems. Contact us for a non-binding conversation about your AI testing and quality strategy.
