Machine Learning solutions are expanding for different applications. With increased competitions of having solutions very quickly, designers are pushed to develop tools for end-to-end Machine Learning development and experiments. Using Machine Learning pipelines tools, users can develop ML solutions with minimal Machine Learning knowledge. The leading U.S. based low-cost programmable logic provider offering solutions across the network from the Edge to the Cloud across the industries such as growing communications, computing, industrial, automotive and consumer markets was looking to develop framework for end-to-end Machine Learning pipeline. The client approached Softnautics for developing a dockerized user friendly web application to streamline Machine Learning development which supports dataset creation, pre-processing, model training, inference, experiments comparison and deployment on edge devices. Subsequently, by using the right engines for the right tasks and devices Softnautics provided a user friendly robust and scalable compiler with the optimal levels of performance. They were able to keep the engineering schedules on track to deliver the designs as scheduled to meet their market commitments.