Design and evaluation of a subsystem (mircoservice) to create a deployment client for AI pipelines based on docker containers using gRPC

Before docker container technology, manual deployment of an application is complex, resource, and time-consuming. With the help of Kubernetes, it is possible to automatically deploy and manage the AI pipelines on a standard cluster. The thesis aims to provide the link between the AI4EU experiment platform catalog from the execution environment to make the system more scalable. In this thesis, we design a solution and implement the Kubernetes client, which takes the AI pipelines topology as input from the catalog and constructs the deployment and service for all the nodes of the AI pipeline for the execution environment. Kubernetes client also generates a container specification based on the pipelines topology, which the orchestrator uses to execute the pipeline. Different AI pipeline s are deployed in separate namespaces with the help of a generic deployment script supporting standard Kubernetes cluster and minikube. The Kubernetes client tested on simple, advance, and hybrid AI pipelines and is also integrated with the production environment of the AI4EU experiment platform and gets feedback from the AI community of this platform.