The Center for Internet Security (CIS) benchmark for Kubernetes consists of secure configuration guidelines especially for Kubernetes infrastructure set-up. These benchmarks encapsulate best practice security recommendations for configuring Kubernetes to support a strong security posture. The CIS Kubernetes Benchmark is written for the open source, upstream Kubernetes distribution and intended to be as universally applicable across distributions as possible.
In this blog, we describe how our customers perform CIS benchmark scans of their fleet of Kubernetes clusters using Rafay.
Many of our customers use Kubernetes extensively for AI/ML use cases. This is one of the reasons why we have turnkey support for Nvidia GPUs on EKS, AKS, Upstream Kubernetes in on-prem data centers. Recently, we have had several customers look at adding support for Generative AI to their applications. Doing so will require looking at a slightly different technology stack.
Traditional relational databases are adept at handling structured data. They do this by storing data in tables. However, AI use cases are focused on handling unstructured data (e.g. images, audio, and text). Data like this is not well suited for storage and retrieval in a tabular format. This critical technology gap with relational databases has opened the door for a new type of database called a Vector Database that can natively store and process vector embeddings. The rapid rise of large-scale generative AI models has further propelled the demand for vector databases.
In this blog, we will review why vector databases are well suited and critical for AI and Generative AI. We will then look at how you can deploy and operate vector databases on Kubernetes using the Rafay Kubernetes Operations Platform in just one step.