Efficient Auto Scaling of Pods in Kubernetes: Accelerating Continuous Delivery with KEPTN

Model deployment in machine learning has become recognised as an emerging field of research in recent years. It can be viewed as a method similar to conventional software development. Organisations can move more quickly and smoothly when development and operations (DevOps) practises like Continuous Integration and Continuous Delivery (CI/CD) are used. Various software development approaches are available for enterprise applications. Micro-services architecture has become very popular among these accessible software development approaches. Enterprise applications, which are frequently sophisticated and require out-of-the-box scalability and low latency, integrate well with the micro-service design. For enterprise applications, microservices in a containerized cloud environment are particularly helpful. It is also extremely important from a software development standpoint in the software business to find and fix errors early on in the development process rather than once they have reached the level of the production environment. Kubernetes is a creative and well-integrated platform solution for running and controlling containerized applications gracefully and effectively. Developers can easily build and maintain clusters with the managed Kubernetes service known as AKS. It makes containerized application deployment and management possible. AKS offers serverless Kubernetes, a continuous integration and delivery experience that is integrated, as well as enterprise-level security and governance. Users of Microsoft Azure will find that AKS offers several appealing features that make it simple to set up, administer, scale, and monitor an AKS cluster.