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How to Perform Quality Assurance and Testing for ML Projects?

Testing and Quality Assurance are two most crucial steps that are more often than not neglected or underestimated. With newer and more advanced technologies engulfing the IT industry, it is important to devise new ways of testing and make sure that software applications remain bug-free and robust.

Machine Learning is changing the way software products and applications think and respond to queries. In the strife to provide our machines with our intelligence, we are empowering our software to think for them and proactively learn based on experiences, just what we do!

Machine Learning results in areas of image, speech, and text recognition, disease detection, human extension, error reduction, and stock prediction. It has a wide array of applications in all major industries in the world like Healthcare, Retail, Life Sciences, Manufacturing, Enterprise, Education, Medicinal sciences, research and development, and Finance and Accounting.

When we talk about testing these Machine Learning projects, we are often confused about the ways in which this can be tackled. Software Testing Services, by definition, is a fairly straightforward task. For every input, there is a definite output. We enter values into the software application, choose to make some processing, and then check the outcome. On the basis of the expected outcome and the actual outcome, we ensure the rightness or wrongness of a software products feature.

The main point here is that we already have in mind the expected output. But, Machine Learning is one scenario that does not depend on the output in a test case. Machine learning is based on neural networks which are layered algorithms whose variables are adjusted according to a learning process. The learning process effectively comprises of using known data inputs to get the outputs which are then compared to the known results. When the algorithm code reflects the known results with the desired degree of accuracy, we freeze the algebraic coefficients and generate the production code.

Main points to be taken care of by testers:

It would be easy to learn it through a video, so let's watch it.

You may follow these steps to ensure efficient testing of ML applications:

For a start, these steps are good to go. Machine learning applications require that the testing practices and results are changed to accommodate their differences from traditional software applications.

Learn more about DevOps to Bring Development and Operations Closer Together here!

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