Yes, You heard it right. My title is "How to use a machine learning algorithm for different input data?",

For example, we have an algorithm called "Email spams detector". Okay, so how to apply the algorithm for 1M users with different input data? because each user creates its own data. So instead of training 1M for every single user. Do we have any method to train once for different input data?

To answer what do I mean by different input data:

For example, I have a time series data collected from a server called "Server A", the data consists of attributes like (CPU level, ram level, protocol...). Next, I put the data collected from Server A to "model X" to predict the server's usage. Now I have a data center with hundreds of servers. Are there any ways to train these hundreds of servers' data once with the same model (model X)? Of course, I don't want to train hundreds of times with model X to get the prediction.


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