提出螺旋模型提升数据科学项目迭代效率与灵活性。
Spiral Model Technique For Data Science & Machine Learning Lifecycle
- 采用螺旋式迭代设计，融合敏捷与循环思维。
- 支持明确目标的业务问题，实现高效闭环优化。
- 适合需快速响应的商业数据项目，提升交付速度。
数据分析在现代企业中扮演关键角色。公司通过适配数据科学生命周期以提升生产力并增强竞争力。数据科学与机器学习生命周期由一系列项目步骤构成，通常呈现线性或循环模式。传统流程在完成周期后可重新启动，但缺乏对目标导向问题的针对性。本文提出一种新方法——螺旋技术，强调灵活性、敏捷性与迭代性，将数据科学生命周期更好地融入具有明确目标的商业问题中，使项目能更高效地推进与优化。
原文摘要 · Abstract (English)
Analytics play an important role in modern business. Companies adapt data science lifecycles to their culture to seek productivity and improve their competitiveness among others. Data science lifecycles are fairly an important contributing factor to start and end a project that are data dependent. Data science and Machine learning life cycles comprises of series of steps that are involved in a project. A typical life cycle states that it is a linear or cyclical model that revolves around. It is mostly depicted that it is possible in a traditional data science life cycle to start the process again after reaching the end of cycle. This paper suggests a new technique to incorporate data science life cycle to business problems that have a clear end goal. A new technique called spiral technique is introduced to emphasize versatility, agility and iterative approach to business processes.
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