The deep learning world roughly can be broken down into two pursuits: training and inferencing. Training involves giving
the computer instructions on how to learn to do something; in other words, teaching the model to learn and develop accuracy.
Inferencing is about running the model on brand new data in an application. You input the model into software code and gain
some insight at the edge or point of sale. Training and inferencing are iterative processes. As your model comes across new data,
it must be continually refreshed and retrained. Some companies retrain their models as frequently as daily to compete with
the ever-evolving threat of fraud.

Windows Server 2019: The operating system that bridges on-premises and cloud.

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