Agile Methodologies for AI-Driven Application Development

Abstract

Agile methodologies have indeed become a widely adopted approach in the modern world of software engineering for its flexibility and iterative development / customer orient approach. With the advent of artificial intelligence (AI) and artificial intelligence-based applications, the traditional methods of software development may be too static to keep pace with the dynamic requirements, complicated workflow and continuous learning capabilities of such an application. Integrating Agile principles into the application development of AI are also a designed structure and adaptability that enables rapid prototyping, modifying a gradual enhancement of the models, develop through collaboration, and constant incorporation of a feedback. This research article is all about the role of Agile methodologies in the development of AI applications, some of the best practices to consider, challenges and strategies to implement the iterative AI applications workflow. It covers the idea that Agile practices such as Scrum, Kanban, and Extreme Programming (XP) help to gather data, model training, validation, model deployment and model iteration for AI projects. Additionally, the study also analyzes the impact that Agile has on mitigating the risks of the development, increase the quality of the model and increase the collaboration of the team in the AI centric software project. Through a synthesis of existing researches, empirical findings, the article provides with insights on good integration of Agile principles and AI development lifecycles.