In addition to the previous answer: According to CRISP-DM, eventually we want to deploy our model and operate it so solve our business problem.
As @Muhammad_John_Abbas mentioned correctly, the performance of the model on test data (which was never seen before) is your litmus test that has to be successful to have sufficient evidence your machine learning pipeline performs well enough on new data. If OK you can go for a deployment, e.g. in the cloud.
In reality you also have:
- distribution shifts of data (think of traffic / remote work during the pandemic situation in 2020)
- IoT swarm intelligence applications which are designed to get better and more powerful over time as self learning systems like this one
This means in reality you have to deploy often, e.g. after a new training where you incorporated new knowledge into your model.
Also in this situation you need to conduct your litmus test with test data which were never seen before, before deployment and operations. (A test data set from a previous litmus test would be an issue since this information already made it somehow into the previous process and is actually not „new“).
I hope this view helps, @mehmet_baki_deniz. Have a good one!
Best regards
Christian