The following figure illustrates a machine learning pipeline at a conceptual level. However, real-life ML pipelines are a lot more complicated, with several models being trained, tuned, combined, and so on:
The next figure shows the core elements of a typical machine learning application split into two parts: the modeling, including model training, and the deployed model (used on streaming data to output the results):
Typically, data scientists experiment or do their modeling work in Python and/or R. Their work is then reimplemented in Java/Scala before deployment in a production environment. Enterprise production environments often consist of web servers, application servers, databases, middleware, and so on. The conversion of prototypical models to production-ready models results in additional design and development effort that lead...
