Optimize AI/ML workloads for sustainability: Part 3, deployment and monitoring

We're celebrating Earth Day 2022 from 4/22 through 4/29 with posts that highlight how to build, maintain, and refine your workloads for sustainability. AWS estimates that inference (the process of using a trained machine learning [ML] algorithm to make a prediction) makes up 90 percent of the cost of an ML model. Given with AWS you pay for what you use, we estimate that inference also generally equates to most of the resource usage within an ML lifecycle. In Part 3, our final piece in the series, we show you how to reduce the environmental impact of your ML workload once your model is in production. If you missed the first parts of this series, in Part 1, we showed you how to examine your workload to help you 1) evaluate the impact of your workload, 2) identify alternatives to training your own model, and 3) optimize data processing.