Design and Implementation of an AutoML FrameWork on Cloud Computing Infrastructure

Automated Machine Learning (AutoML) consists of stages to automate the entire pipeline of machine learning. AutoML is especially useful for teams/organizations that does not necessarily have sufficient expertise on the  AI/ML domain but expected to implement practical ML applications based on given data sources. There are already a variety frameworks for AutoML such as H2O, TPOT, Auto-SKLearn and AutoGluon.
In this project a complete AutoML service will be designed and implemented using cloud computing techniques where the users can interact with this system using a web dashboard as well as a separate API/SDK in the form of AIaaS. 
This is an end-to-end project where (i) requirement analysis, (ii) cloud architecture design and  (ii) detailed performance benchmarking phases will be included.