Theodore Meynard

I will present the challenges we encountered while migrating an ML model from batch to real-time predictions and how we handled them. In particular, I will focus on the design decisions and open-source tools we built to test the code, data and models as part of the CI/CD pipeline and enable us to ship fast with confidence.

At GetYourGuide we build a marketplace for travel experiences. The ranking of activities on the platform is one of the most essential machine-learning products for the business. In this talk, I will explain how we gradually migrated our ranking from global precomputed scores to a live reranking service. Building such a service with high availability requirements and constant modifications brings challenges. I will dive into the design decisions and open-source tools we built to enable us to test code, data, and models as part of the CI/CD pipeline. It allows us to ship fast with confidence without losing ourselves in cumbersome tests and/or a mocking hell. At the end of the talk, you will have actionable insights you can apply to your Machine Learning products and understand how to introduce good MLOps practices using open-source tools.

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.