  This is an article or technical note which is intended to provides an insight
journey of Machine Learning Systems (MLS) testing, its evolution, current
paradigm and future work. Machine Learning Models, used in critical
applications such as healthcare industry, Automobile, and Air Traffic control,
Share Trading etc., and failure of ML Model can lead to severe consequences in
terms of loss of life or property. To remediate this, developers, scientists,
and ML community around the world, must build a highly reliable test
architecture for critical ML application. At the very foundation layer, any
test model must satisfy the core testing attributes such as test properties and
its components. This attribute comes from the software engineering, but the
same cannot be applied in as-is form to the ML testing and we will tell you
why.
