Administrators of machine learning (ML) workloads are focused on ensuring that users are operating in the most secure manner, striving towards a principal of least privilege design. They have a wide variety of personas to account for, each with their own unique sets of needs, and building the right sets of permissions policies to meet those needs can sometimes be an inhibitor to agility. In this post, we look at how to use Amazon SageMaker Role Manager to quickly build out a set of persona-based roles that can be further customized to your specific requirements in minutes, right on the Amazon SageMaker console. Role Manager offers predefined personas and ML activities combined with a wizard to streamline your permission generation process, allowing your ML practitioners to perform their responsibilities with the minimal necessary permissions. If you require additional customization, SageMaker Role Manager allows you to specify networking and encryption permissions for Amazon Virtual Private Cloud (Amazon VPC) resources and AWS Key Management Service (AWS KMS) encryption keys, and attach your custom policies.

One of the downsides to the recent revival and popularity of Artificial Intelligence (AI) is that we see a lot of vendors, professional services firms, and end users jumping on the AI bandwagon labeling their technologies, products, service offerings, and projects as AI products, projects, or offerings without necessarily being the case. On the other hand, there isn't a well-accepted delineation between what is definitely AI and what is definitely not AI. This is because there isn't a well-accepted and standard definition of what is artificial intelligence. Perhaps it is best to start with the overall goals of what we're trying to achieve with AI, rather than definitions of what AI is or isn't. Since the beginning of the AI in the 1950s, the goals of intelligent systems are those that mimic human cognitive abilities.