Helping entry-level data scientists transform their ideas into industrial-grade applications
Going back to my student days, my role in data science projects was a bit like an alchemist, experimenting with fancy AI models to uncover relationships between variables in my field's data. Powerful AI algorithms consistently surprise us by outperforming traditional statistical methods and physics-based models. But the real challenge started when I became an AI engineer in his 2022 industry. Since then, the data science technology stack has rapidly expanded into areas I was unfamiliar with. My first challenge in the industry was to ship a model into production with reliability, maintainability, and scalability requirements. As I reflected on my struggles, I realized that converting an AI model from a prototype to a production-ready application is a combination of:
- great design patterns
- robust code
- Efficient implementation strategy
This article is a comprehensive guide summarizing seven important topics from previous sub-articles. Each topic will discuss one aspect of the development and deployment of data science projects at an industry level.
- Modularize your code
- data validation
- abstraction
- configuration management
- web service
- API documentation
- Docker and the cloud
This article uses a streamflow prediction application as a case study and dives into each topic with core concepts and demos to provide entry-level data scientists with powerful tools to advance their career skills. . Start your AI engineering journey!
Modularization involves dividing a program into smaller, independent modules. Modular code allows you to resolve errors within a specific module, making it easier to maintain and debug. Modular code also improves extensibility, as you only need to change the code in specific modules when adding functionality. Furthermore, when you write the code…