A New Architectural Model Shift Beyond the Cloud in Python Using PS

Abstract

Nowadays, data science initiatives are typically generated in an unstructured manner, making replication difficult. Transitioning from an experimental to a production environment is also difficult. Data scientists may transfer a pipeline from a local environment to the cloud using operational techniques like containerization, continuous deployment, and cloud orchestration. Recognizing the difficulties associated with establishing those procedures, this study proposes a framework for easier experiment monitoring and machine learning operationalization by combining existing and wellsupported technologies. Python libraries, Python environment, and Command Line Interface are all available. (CLI) are only a few of the technologies available. The framework offers an opinionated approach for designing and running experiments in a local or remote environment. PyScript includes an automatic tracking system for the most famous Data Science libraries: beautiful soup4, NumPy, Pandas, scikit-learn, scikit-image, and matplotlib, first-class support for distributed training and hyperparameter optimization, and a Command Line Interface (CLI) for packaging and running projects inside web browser.