1 research outputs found
Optimizing the AI Development Process by Providing the Best Support Environment
The purpose of this study is to investigate the development process for
Artificial inelegance (AI) and machine learning (ML) applications in order to
provide the best support environment. The main stages of ML are problem
understanding, data management, model building, model deployment and
maintenance. This project focuses on investigating the data management stage of
ML development and its obstacles as it is the most important stage of machine
learning development because the accuracy of the end model is relying on the
kind of data fed into the model. The biggest obstacle found on this stage was
the lack of sufficient data for model learning, especially in the fields where
data is confidential. This project aimed to build and develop a framework for
researchers and developers that can help solve the lack of sufficient data
during data management stage. The framework utilizes several data augmentation
techniques that can be used to generate new data from the original dataset
which can improve the overall performance of the ML applications by increasing
the quantity and quality of available data to feed the model with the best
possible data. The framework was built using python language to perform data
augmentation using deep learning advancements