Hello,
If you scale data to have values between 0 - 1, you usually use the normalization formula

Example: 1, 2, 1, 4 (min = 1, max = 4) --> 0.0, 0.25, 0.00, 1.0
When your model has been trained, new data needs to be scaled too before it can serve as input for the model. You do this by plugging the new values again into the formula, but using the min and max values of the data you used for training.
Example: 3 (min = 1, max = 4) -- > 0.75
There is one caveat here: If the min and max values are not the natural limits of the data, then new values might be larger than the max of the training or smaller than the min. In that case you'll end up with a scaled new value > 1 or < 0, respectively. You need to clip these to 1 or 0 before putting them into your model.
Example: 5 --> 1.33 (needs to be clipped) --> 1.00
Example: 0 --> -0.33 (needs to be clipped) --> 0.00
Hope this helps,
PJ