batch learning vs on-line learning

Batch learning:
  you create your model in the developmental site and you deploy that model to online server . It worked but only with new data only
 take an example of Netflix recommendation system: there is update of movies and your model is working with old data and already new movies are added so theremust be provision to update your batch system model. 
If you update your model in 24-24 hours by collecting new data from particular deployment system or in 1 week than also there are many problems of this system such as:

1. lots of data
    there could be lots of data being piled up in your model and there is possibility at some period of time model cant process that much amount of data
2. hardware limitation:
   if you are in online service and one person is in Mustang no internet service and he stays there for 1 week and is currently watching Netflix let us assume : your model has already updated in 24 hours and muktinath person still is in old model case 
  
 another example is: if a model is trained in a device that goes to outer space and there is no internet connection say your model is already updated in 24 hours but not that spaceship device

3.availability:
    your model   is updated only in 24 hours so for real time service it is not that much effective


online learning:
   you perform all the activities over online and it reduces error of the model

   

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