The Role of Data Quality Assurance in AIML Model Deployment Engineering Frameworks and Best Practices
DOI:
https://doi.org/10.63282/3050-922X.ICRCEDA25-117Keywords:
Data Quality Assurance (DQA), AI/ML Model Deployment, Data Validation, Bias Detection, Continuous Monitoring, Automated Testing, Model Performance, Regulatory Compliance, Quality Metrics, MLOpsAbstract
In the rapidly evolving field of AI/ML, the deployment of models into production environments necessitates stringent Data Quality Assurance (DQA) measures. DQA encompasses a comprehensive framework of processes and best practices aimed at ensuring the accuracy, consistency, and reliability of data throughout the AI/ML lifecycle. This paper explores the critical role of DQA in AI/ML model deployment, examining its impact on model performance, ethical considerations, and regulatory compliance. We discuss the integration of DQA within AI/ML engineering frameworks, highlighting methodologies for data validation, bias detection, and continuous monitoring. Furthermore, the paper presents best practices for implementing DQA, including automated testing, collaborative efforts between data scientists and QA teams, and the establishment of clear quality metrics. By adopting these practices, organizations can enhance the reliability and trustworthiness of their AI/ML models, fostering greater acceptance and value in real-world applications
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