International Journal on Science and Technology

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A Modular and Reusable Architecture for an Industry-Grade Machine Learning Pipeline

Author(s) Rahul Roy Devarakonda
Country India
Abstract Large-scale machine learning (ML) applications have made modular and reusable architectures essential for guaranteeing efficiency, scalability, and maintainability. The industry-grade ML pipeline presented in this paper was created using a modular design, which allows for smooth integration, interoperability, and adaptation across different domains. The suggested design guarantees low resource overhead and optimal performance while facilitating effective data preparation, feature engineering, model training, evaluation, and deployment. The framework improves computational efficiency and speeds up model iteration cycles by utilizing cutting-edge optimization, automation, and parallelization approaches. It also incorporates software engineering best practices to guarantee scalability and resilience, tackling important issues in ML workflow automation. It is a future-proof option for businesses because of its modular design, which makes it simple to adapt new frameworks and algorithms. Additionally, in order to comply with industry norms for using ML models in production settings, the suggested method takes compliance and security measures into account. When compared to conventional monolithic ML pipelines, experimental validation shows notable gains in performance, adaptability, and efficiency.
Keywords Machine Learning Pipeline, Reusable Framework, Scalable AI Systems, AutoML, Edge AI Deployment, Explainable AI
Field Engineering
Published In Volume 9, Issue 1, January-March 2018
Published On 2018-01-03
Cite This A Modular and Reusable Architecture for an Industry-Grade Machine Learning Pipeline - Rahul Roy Devarakonda - IJSAT Volume 9, Issue 1, January-March 2018.
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