Deployment Strategies
Explore various deployment strategies essential for maintaining AI products in production. Understand how to manage model retraining, monitor performance, and select the right approach such as shadow deployment, A/B testing, or canary releases based on product goals and user impact.
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 ...