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Engineering Robust Machine Learning Systems: a Framework for Production-grade Deployments

Year: 2024

Type: article

Abstract: Production machine learning systems require a comprehensive engineering approach beyond model accuracy metrics.This article presents a systematic framework for developing and maintaining robust ML systems in production environments.The article introduces a holistic methodology encompassing critical aspects of production ML systems: comprehensive monitoring infrastructure, feature engineering pipeline design, deployment strategies, and resource management protocols.

Source: INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND INFORMATION TECHNOLOGY

DOI: 10.34218/ijrcait_07_02_208

Author: Aditya Singh

Institution: University of Wisconsin–Madison

Language: English

Topic: Software System Performance and Reliability

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: bronze

OpenAlex ID: W4405854986

FWCI: 0.343

Cites: 10

Cited by: 2

Related to: 10