Helping Entry-Level Data Scientists to Transform Ideas into Industrial-Level applications

Wencong Yang

Towards Data Science

Seven Requisite Skills for Navigating from Data Science to Applications | by Wencong Yang | Apr, 2024 - image  on
Image by author (Ideogram)

Back in my college days, my role in data science projects was like an alchemist—experimenting with fancy AI models to dig out the relationship among variables from data in my major. Powerful AI algorithms consistently amazed me by outperforming traditional statistical methods and physical-based models. However, the real challenge began when I became an AI engineer in the industry in 2022. From then on, the technology stack of data science expanded rapidly into fields that I was unfamiliar with. My first challenge in the industry was to ship a model to the production environment, with the requirements of reliability, maintainability, and scalability. Retrospecting my struggles, I realize transforming AI models from prototypes to production-ready applications is nothing more than a combination of

  • Good design patterns
  • Robust code
  • Efficient deployment strategies

This article is a comprehensive guide summarizing from seven key topics from my earlier sub-articles. Each topic explores one aspect of developing and deploying data science projects at an industry level:

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