Clean Architecture: How to Structure Your ML Projects to Reduce Technical Debt

Software engineering principles are frequently mentioned as a solution to data science’s productivity problem. Unfortunately, rarely in a comprehensive format to be actionable or adopted for data-intensive use. In this talk, I will present the Clean Architecture framework that enables practitioners to structure their projects and manage changes throughout their lifecycles. The audience will also learn about a minimum set of programming concepts to make this a reality. The key takeaway is that, as a data scientist, you can take care of your codebase with only a few techniques and a little effort.