Deployment Strategies

Production setup

Once we’re happy with the models we’ve chosen, including the performance and error rate, we’ve got a good level of infrastructure to support our product and chosen AI model’s use case; we’re ready to go to the last step of the process and deploy this code into production. Keeping up with a deployment strategy that works for our product and organization will be part of the continuous maintenance. We’ll need to think about things such as how often we’ll need to retrain our models and refresh our training data to prevent model decay and data drift. We’ll also need a system for continuously monitoring our model’s performance, so this process will be really specific to our product and business, particularly because these periods of retraining will require some downtime for our system.

Deployment process

Deployment is going to be a dynamic process because our models are trying to ...