Summary
In this chapter, we discussed a variety of strategies and solutions to manage the overall security, compliance, and governance of ML environments and systems. We started by going through several best practices to improve the security and compliance of ML environments. After that, we discussed relevant techniques on how to preserve data privacy and model privacy. Toward the end of this chapter, we covered different solutions using a variety of AWS services to establish ML governance.
In the next chapter, we will provide a quick introduction to MLOps pipelines and then dive deep into automating ML workflows in AWS using Kubeflow Pipelines.
