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.
Speakers: Theodore Meynard