04-18, 10:30–11:00 (Europe/Berlin), B09
Even if every data science work is special, a lot can be learned from similar problems solved in the past. In this talk, I will share some specific software design concepts that data scientists can use to build better data products.
Data science has evolved from magic models measured by accuracy to software components with an ML core. As such, data scientists’ work should also follow best practices and have a suitable architecture.
It is where design patterns can help advance the discipline. A design pattern is a reusable solution to a commonly occurring problem. It is not a concrete piece of code that can be used directly but identifying a pattern help understand the problem and also help build a common language around it.
In this talk, I will share some specific software design concepts that data scientists can use to build better data products. I will not focus on patterns that will improve the performance of your model (you can already find a lot about it online) but on the ones that will help you bring your model to production.
Novice
Abstract as a tweet –I will share some specific software design concepts that can be used by data scientists to build better data products.
Expected audience expertise: Domain –Intermediate
Theodore Meynard is a data scientist at GetYourGuide. He works on our ranking algorithm to help customers to find the best activities to book and locations to explore. He is one of the co-organisers of the Pydata Berlin meetup. When he is not programming, he loves riding his bike looking for the best bakery-patisserie in town.