Overview of Different Approaches to Deploying Machine Learning Models in Production
There are different approaches to putting models into productions, with benefits that can vary dependent on the specific use case. Take for example the use case of churn prediction, there is value in having a static value already that can easily be looked up when someone call a customer service, but there is some extra value that could be gained if for specific events, the model could be re-run with the newly acquired information.
There is generally different ways to both train and server models into production:
- Train: one off, batch and real-time/online training
- Serve: Batch, Realtime (Database Trigger, Pub/Sub, web-service, inApp)
Each approach having its own set of benefits and tradeoffs that need to be considered.