Deployment

At this stage, we should have done almost all of the analysis and development needed for building an anomaly detector, or in general a data product using deep learning.

We are only left with final, but not less important, step: the deployment.

Deployment is generally very specific of the use case and enterprise infrastructure. In this section, we will cover some common approaches used in general data science production systems.

POJO model export

In the Testing section, we summarized all the different entities in a machine learning pipeline. In particular, we have seen the definition and differences of a model, a fitted model and the learning algorithm. After we have trained, validated, and selected the final model, we have a final fitted version of it ready to be used. During the testing phase (except in A/B testing), we have scored only historical data that was generally already available in the machines where we trained the model.

In enterprise architectures, it is common to have...