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 Clear 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.
Formerly he was Head of Data Science at Arkera, a fintech startup in London, where he built market intelligence products with Natural Language Processing for Tier 1 investment banks and hedge funds. Prior to that, Laszlo worked in mobile gaming for King Digital (makers of Candy Crush), specializing in player behavior and monetization. He started his career as a quant researcher implementing trading strategies at multiple investment managers.
