Abstract: International Visa programs (i.e. U.S. H-1B) have extremely high application volumes with limited quotas and rigorous variable outcomes. Complexity and uncertainty propel computer-aided decision systems. We launch an end to-end MLOps platform to provide real-time visa approval predictions. Our pipeline integrates data pre-processing (Pandas), training of the model (Scikit-learn), containerized deployment (Docker), and ongoing delivery (GitHub Actions) on AWS. The models and data reside in AWS S3 and EC2, while being monitored by Cloud Watch. This combined approach offers scalable, reproducible deployment of predictive models. Experiments illustrate system has good accuracy (similar to previous work) and can be retrained periodically with minimal human intervention. In brief, we present an end-to-end ML pipeline that bridges the gap between application and operational utilization, to the benefit of immigration authorities, employers, and candidates alike.Automated accounting.
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DOI:
10.17148/IJARCCE.2025.14567
