![[2602.17102] Operationalization of Machine Learning with Serverless Architecture: An Industrial Operationalization of Machine Learning with Serverless Architecture: An Industrial Implementation for Harmonized System Code Prediction](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.17102] Operationalization of Machine Learning with Serverless Architecture: An Industrial Operationalization of Machine Learning with Serverless Architecture: An Industrial Implementation for Harmonized System Code Prediction
Summary
This paper presents a serverless MLOps framework for the complete ML lifecycle, focusing on Harmonized System code prediction, achieving high accuracy and cost-efficiency.
Why It Matters
The operationalization of machine learning using serverless architecture is crucial for industries facing compliance challenges. This framework enhances efficiency, reduces operational costs, and ensures scalability, making it relevant for businesses in global trade and logistics.
Key Takeaways
- Introduces a serverless MLOps framework for ML lifecycle management.
- Achieves 98% accuracy in HS code prediction using various deep learning models.
- Emphasizes cost-efficiency and operational scalability over traditional models.
- Incorporates automated A/B testing for dynamic model selection.
- Provides a replicable blueprint for enterprises to optimize ML operations.
Computer Science > Machine Learning arXiv:2602.17102 (cs) [Submitted on 19 Feb 2026] Title:Operationalization of Machine Learning with Serverless Architecture: An Industrial Operationalization of Machine Learning with Serverless Architecture: An Industrial Implementation for Harmonized System Code Prediction Authors:Sai Vineeth Kandappareddigari, Santhoshkumar Jagadish, Gauri Verma, Ilhuicamina Contreras, Christopher Dignam, Anmol Srivastava, Benjamin Demers View a PDF of the paper titled Operationalization of Machine Learning with Serverless Architecture: An Industrial Operationalization of Machine Learning with Serverless Architecture: An Industrial Implementation for Harmonized System Code Prediction, by Sai Vineeth Kandappareddigari and 6 other authors View PDF HTML (experimental) Abstract:This paper presents a serverless MLOps framework orchestrating the complete ML lifecycle from data ingestion, training, deployment, monitoring, and retraining to using event-driven pipelines and managed services. The architecture is model-agnostic, supporting diverse inference patterns through standardized interfaces, enabling rapid adaptation without infrastructure overhead. We demonstrate practical applicability through an industrial implementation for Harmonized System (HS) code prediction, a compliance-critical task where short, unstructured product descriptions are mapped to standardized codes used by customs authorities in global trade. Frequent updates and ambiguous descriptio...
