Software Engineering Best Practices for Writing Maintainable ML Code …

Advanced coding tips for data scientists

Unlike traditional software engineering projects, ML codebases tend to lag behind in code quality due to their complex and evolving nature, leading to increased technical debt and difficulties in collaboration. Prioritizing maintainability is important to create robust ML solutions that can adapt, scale, and deliver value over time.

In recent years, machine learning has taken the world by storm, transforming industries from healthcare to finance and more. As more organizations jump on the ML bandwagon to discover new possibilities and insights, the significance of writing maintainable and robust ML code becomes crucial. By crafting ML code that’s easy to work with and stands the test of time, teams can collaborate better and guarantee success as models and projects grow and adapt. The following section will…