FREMONT, CA: Quality assurance (QA) is a primary requirement every organization must follow when launching a product in the market.  It is an integral part of every business process which assures the involved stakeholders about the quality of the product. Established organizations often carry out the deployment of its products in iterative phases according to the evolving needs of the consumers. As with every other product, artificial intelligence (AI) projects go through the iterative stages of design, development, testing, and deployment.

However, the methodologies involved in AI productions are distinct from traditional products. Testing is an essential factor when developing AI projects. When designing an AI model, the developers have to verify the quality of the training data and assess its usefulness in accurately classifying or regressing data with the required generalization, while eliminating the probabilities of overfitting or underfitting it.

The data testing is conducted through validation techniques to ensure the amalgamation of the algorithm, data, hyperparameter configuration, and associated metadata. If the validation results predict a negative outcome, the developers alter the hyperparameters and rebuild the model with augmented training data. After the completion of the validation phase, the testing data is used to verify the functioning of the model.

Apart from validation, the training phase includes the testing of the AI algorithm. It is the base on which the training data and hyperparameters are built. Since the algorithms are already available in AI libraries, there is no reason to code the algorithm from scratch. If no alterations are done to the library-based algorithms, there is no need for the QA to include algorithm testing. The training phase testing of AI consists of the evaluation of training data and the hyperparameter configuration data. The validation methods for hyperparameter testing include K-fold cross-validation.

Another critical step in the QA of the AI model is the assessment of the data quality and wholeness. The training model has to represent the reality of generalization. It also needs to be vetted for informational and human-induced bias in the training data. It is imperative for the successful functionality of the AI model that the training model comprises a representative sample of the real world. AI projects are based on data; hence, developers need to focus on data-centric methodology when testing the AI models.

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