Summary
In this chapter, we provided best practices and tips for designing an end-to-end CV pipeline. We discussed the importance of defining a problem that CV can solve before moving toward the data collection stage of your project. In addition, we highlighted techniques for preprocessing data, training, evaluating, tuning, deploying, and monitoring a model. Next, we covered how to develop an MLOps strategy. Lastly, we summarized the AWS Well-Architected Framework and addressed considerations for architecting secure, scalable, reliable, and efficient CV workloads. In the next chapter, we will define AI governance and detail how to establish a framework for applying governance to your CV workloads.