为机器学习操作制定或购买策略 - MLOps
Diego Nogare1,2, Ismar F Silveira1, Renato Banzai2
1Universidade Presbiteriana Mackenzie, Programa de Pós-Graduação em Engenharia Elétrica e Computação - PPGEEC, Rua da Consolação, 930, 01302-907 São Paulo, SP, Brazil.
Anais da Academia Brasileira de Ciencias
|May 14, 2025
概括
组织在决定是否构建或购买机器学习操作 (MLOps) 解决方案时,必须权衡成本,专业知识和战略一致性. 这项研究指导了MLOps的战略,分析了有效的机器学习模型生命周期管理的工具.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 商业战略 商业战略
背景情况:
- 组织面临着关于机器学习操作 (MLOps) 实施的复杂决策.
- 对于MLOps的"制造或购买"战略,需要平衡成本,质量,专业知识和战略目标.
研究的目的:
- 分析MLOps解决方案的制造或购买策略.
- 为组织提供指南,帮助组织在MLOps能力的内部开发和外部采购之间做出决定.
- 评估各种MLOps工具用于机器学习模型生命周期管理.
主要方法:
- 对MLOps工具进行定性和定量审查.
- 分析包括成本,质量,技术专业知识和战略调整在内的因素.
- 在MLOps中探索产品复杂性,核心能力和风险管理.
主要成果:
- 介绍了一个评估MLOps"做或买"决策的框架.
- 提供了对流行的MLOps平台如MLFlow,Airflow,Kubeflow,Databricks,Dataiku,H2O,AWS,Azure和GCP进行比较的评论.
- 在工具选择中强调了项目特定需求的重要性.
结论:
- 有效的MLOps实施取决于明确的战略和适当的工具选择.
- 了解组织能力和项目要求对于成功采用MLOps至关重要.
- 这项研究提供了关于如何在MLOps环境中获得竞争优势的见解.
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