If your company has developed software before, then you know it’s never as simple as writing some code and put it out in production. Regardless of who the software is for (clients, employees, third parties), doing proper Quality Assurance (QA) is a must. Otherwise, you would be blind to the software’s limitations and may even country wise email marketing list deliver broken or totally unusable products.
As such, QA engineers work all throughout the software development life cycle using agile methodologies and testing all progress in small and iterative increments, making sure that the product always responds to the appropriate goals.
The Role of QA & Testing In AI Projects
If you want to develop AI that actually works, you can’t just throw some training data at an algorithm and call it a day. The role of QA & Testing is to verify the “usefulness” of the training data, and whether or not it does the job we are asking from it.
How is this done? Via simple validation techniques. Basically, QA engineers working with AI need to select a portion of the training data use seasonal data to generate leads throughout the year to use in the validation stage. Then, they put it through a crafted scenario and measure how the algorithm performs, how the data behaves, and if the AI is returning predictive results accurately and consistently.
If the QA team detects significant errors during the validation process, then the AI goes back into development, just like you would do with any other software development project. After some tweaks here and there, the AI comes back into QA until it delivers the expected results.
Artificial Intelligence Needs to Be Tested In Production
However, every AI project will always marketing list have a unique way to manage and process data—and, as we all know, data is growing and changing at all times. This is why the QA approach for AI development extends to the production stage.