How to put machine learning models into production

Description

Summary

• The goal of building a machine learning model is to solve a problem, and a machine learning model can only do so when it is in production and actively in use by consumers.
• Here the question of batch vs. real-time data retrieval comes to mind, and this has to be considered before designing the ML system.
• Your prediction data is TK (define it relative to training data) and it is rarely as neatly packaged as the training data, so you need to consider a few more issues related to how your model will receive data at inference time: In terms of programming languages for prototyping, model building, and deployment, you can decide to choose the same language for these three stages or use different ones according to your research findings.