Systematic Review of Project Life Cycle of AI Tools and AI Enabled Projects: A Study Based on Best Development Practices.

Abstract

The development and deployment of AI tools necessitate a structured approach that adheres to best practices throughout the project life cycle to ensure their effectiveness, reliability, and ethical compliance. This systematic review aims to identify and analyze the best development practices followed in the project life cycle of AI tools, spanning from problem identification to maintenance and monitoring. By examining a diverse array of scholarly articles, case studies, and industry reports, this study synthesizes key practices and methodologies that contribute to successful AI projects. The findings highlight crucial practices in various stages, including data collection and preparation, model development and training, model evaluation and validation, deployment and integration, and ongoing maintenance. This review provides valuable insights and practical guidelines for AI practitioners and researchers, facilitating the optimization of AI development processes and promoting the creation of robust, scalable, and ethically sound AI solutions.