AI Product Development Lifecycle: A Framework ML - Based Products

Developing AI - driven products presents unique challenges, including model drift, bias, and regulatory compliance. Unlike traditional software, ML - based systems require continuous monitoring, adaptation, and governance. This paper introduces a structured AI product development framework that integrates MLOps, automation, and risk mitigation strategies to address these challenges. The framework defines key stages, including problem identification, data acquisition, model training, deployment, and ongoing monitoring. By incorporating industry best practices, compliance strategies (e. g., EU AI Act, NIST risk management), and real - world case studies (e. g., bias in IBM Watson and financial model drift), this study provides a roadmap for AI engineers and business leaders. Adopting this framework helps organizations streamline AI development, improve model fairness and security, and accelerate product deployment while ensuring regulatory alignment. AI teams face challenges such as model drift, bias, scalability issues, and evolving regulatory requirements. To address these, this paper proposes a structured AI product development framework that integrates MLOps, automation, and compliance measures to enhance model reliability, fairness, and deployment efficiency. The framework provides a standardized approach to AI lifecycle management, covering problem identification, data acquisition, model validation, deployment, and continuous monitoring. It incorporates best practices from the EU AI Act, NIST AI Risk Management Framework, and Explainable AI (XAI) to ensure transparency and compliance. The paper demonstrates practical applications using real - world case studies, such as bias in healthcare AI with IBM Watson and model drift in financial systems. Organizations can streamline AI development, mitigate risks, and deploy scalable, regulation - compliant AI solutions by adopting this framework.