Our thesis aims to develop and implement an AI-based model for face recognition using the Docker container, such that it can be transferable to any IoT platform. The main objective of the thesis is to develop an AI-based face recognition Model (which is implemented following the Deep Learning algorithm)for the security system for making decisions to lock or unlock the door system and to deploy the developed AI Model in a Docker Container on an IoT platform. The main aim of the thesis would be to achieve the edge computing concept that brings the Artificial Intelligence (through our AI model) to the low power Internet of Things (IoT) devices with the help of containerization concept. Containerisation would be similar to the virtualisation. Docker containers are easy to port on various IoT devices (Firefly rk3399). Along with the portability, Docker includes all the dependencies and modules required for running the application in a container. Our research work comprises the methodology of developing the containerised AI model. We have chosen the method of training the algorithm such that it detects the faces captured by our camera, which is connected with the help of CSI connector. The algorithm includes the concept of Deep Learning which is a subset of Artificial Intelligence. The method consists of several steps, for example, Deep learning Algorithm detects the faces from the image, and then the image is converted to a set of gradients. These gradients can be converted again to landmarks to consider the focal points of the image and then the training step is performed using the Support Vector Machine classifier. Finally, the authorised user is recognised. Our research work comprises the methodology of developing the containerised AI model and deploying the containerised application on the Raspberry Pi (IoT device), which consists of the ARM processor. We conclude that the containerised application run with high efficiency, is portable and transferable between multiple platforms, and the containerised application is compatible with multiple architectures (ARM, x86, amd64).