Laszlo Sragner Presents:
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 a framework that enables practitioners to structure their projects and manage changes throughout the product lifecycle at low effort.
Audience will also learn about a minimum set of programming concepts to make this a reality.
The key takeaway for any Data Scientist is that you don't need to be a master programmer to start taking care of your own codebase.
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