Security, Governance, and Compliance Strategies
In the first eight chapters of this book, we focused on getting our machine learning (ML) experiments and deployments working in the cloud. In addition to this, we were able to analyze, clean, and transform several sample datasets using a variety of services. For some of the hands-on examples, we made use of synthetically generated datasets that are relatively safe to work with from a security standpoint (since these datasets do not contain personally identifiable information (PII)). We were able to accomplish a lot of things in the previous chapters, but it is important to note that getting the data engineering and ML engineering workloads running in our AWS account is just the first step! Once we need to work on production-level ML requirements, we have to worry about other challenges concerning the security, governance, and compliance of the ML systems and processes. To solve these challenges, we have to use a variety of solutions...