In AI testing, the QA team’s job goes beyond just traditional testing. They focus on checking how accurate the model is, if it’s fair, and if it’s ethical, while developers concentrate more on the main features of the model.
AI can help change how we test by doing routine tasks, predicting where things might fail, and fixing tests that break. By adding AI to your continuous integration/continuous deployment (CI/CD) pipeline, you can make testing more efficient and flexible.
Tools like TensorFlow Model Analysis and SHAP help check if the models are accurate by seeing if their results match what we expect. By regularly updating and testing them on new data, we can make sure they work well in different situations.
Tools like DeepXplore and SHAP help make sure AI-ML models are working correctly by looking for any mistakes and making sure we can understand how they work. You can also create steps to automatically update and check these models, making sure they keep working well as new data comes in.