Batch Training While not fully necessary to implement a model in production

Batch training while not fully necessary to implement

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performance deteriorates enough that they are called upon to refresh it. Batch Training While not fully necessary to implement a model in production, batch training allows to have a constantly refreshed version of your model based on the latest train.Batch training can benefit a-lot from AutoML type of frameworks, AutoML enables you to perform/automate activities such as feature processing, feature selection, model selections and parameter optimization. Their recent performance has been on par or bested the most diligent data-scientists. Real time training Real- time training is possible with ‘Online Machine Learning’ models, algorithms supporting this method of training includes K-means (through mini-batch), Linear and Logistic Regression (through Stochastic Gradient Descent) as well as Naive Bayes classifier Batch vs. Real-time Prediction When looking at whether to setup a batch or real-time prediction, it is important to get an understanding of why doing real-time prediction would be important. It can potentially be for getting a new score when significant event happen, for instance what would be the churn score of customer when they call a contact center. These benefits needs to be weighted against the complexity and cost implications that arise from doing real-time predictions.
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