Quality assurance of AI models is a must, but how is it done?
ARTIFICIAL intelligence (AI) is a groundbreaking, revolutionary technology with wide applications across industries.
If developed successfully, AI can be deployed into operations where it can work independently to produce exciting business insights.
However, just like any other digital solution, AI must be tested thoroughly from its modular state to ensure that it is explainable, reliable, human-bias-free, and trustworthy.
Most importantly, because AI is a system that is programmed to continuously learn from masses of data input and evolve alongside the data that it feeds on, quality assurance (QA) becomes a fundamental aspect of its development.
Ultimately, deployers need to make sure that the self-learning technology is a great fit for the operations it is built for – from the algorithm used to develop the model to the data harnessed for its training processes.
After all, the goal is to ensure that the model delivers desired results based on accurate metrics or produces outcomes that meet the pre-defined standards of an operational process.
QA testing for AI models revolve around making sure that they can learn and infer data the way they were essentially developed for.
It all starts with the algorithm or the mathematical equation that is used to build the self-learning model. However, at this stage of intelligent machine development, error-free algorithms are readily available.
Developers are only left with the task of choosing one that best fits the application they are building and need to focus the testing efforts towards archiving an appropriate set of training data.
Training data is the core ingredient in making AI ‘work’. Developers use training data to get the model to process and infer data in ways that satisfy the hyperparameter configuration – the standards of AI operation and processing.
To ensure the training data is fit for the model, the data itself has to be tested for quality, completeness, reliability, and validity. This includes identifying and removing any sort of human bias.
Not to mention, in real-world scenarios, the data that AI receives may be a stretch from what it is trained on, so the training data must be diverse enough to prepare it for real-life applications.
Next, the QA team must use the model’s hyperparameter configuration data to ensure that the model will be able to process the training data using the algorithm it is built upon.
In simpler words, a test must be done to ensure that the parameters used to configure the AI model can perform at capacity and meet the desired performance standards.
This is done through a series of validation processes – feeding it with training data and valuing the outcomes (inferences) it produces.
If the outcomes are not up to desired standards, then developers go back to rebuilding the model and feed it with training data again.
Though it may seem complicated, it is important that AI adopters understand how quality is attained from an AI model before it is deployed as a system.
QA testing is not merely a procedure that AI developers must complete, but it is an instrumental process in ensuring that intelligent machines can effectively drive operations to new highs and maximize efficiency.