A pattern built for development in performance testing is known as Test-Driven Machine Learning Development. It is a process that enables the developers to write code and estimate the intended behavior of the application.

The requirements for the Test-Driven Machine Learning Development process are mentioned below-

Detect the change in intended behavior.
A rapid iteration cycle that produces working software after each iteration.
To identify the bugs. If a test is not failing, but still a bug is found, then it is not considered as a bug, it will be considered as a new feature.
Tests can be written for functions and methods, whole classes, programs, web services, whole machine learning pipelines, neural networks, random forests, mathematical implementations and many more.

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