Machine learning model deployment in production as well maintaining the continuous integration and continuous deployment to keep the models updated is a bit more tedious than traditional software/application updates on production.

Like software development life cycle management there are now well-developed methodologies and tools for machine learning life cycle management as well. In this document we are going to cover some of the most common ML model deployment scenarios and go through pros and cons of each one of them which can help someone to decide when to use what approach. We will also present some success stories where we implemented various ML models in production.