Deployment strategies – what do we do with these outputs?

Once you’re happy with the models you’ve chosen (including their performance and error rate), you’ve got a good level of infrastructure to support your product and chosen AI model’s use case; you’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 your product and organization will be part of the continuous maintenance we’ve outlined in the previous section. You’ll need to think about things such as how often you’ll need to retrain your models and refresh your training data to prevent model decay and data drift. You’ll also need a system for continuously monitoring your model’s performance so this process will be really specific to your product and business, particularly because these periods of retraining will require some downtime for your system.

Deployment is going...