PyConDE & PyData Berlin 2024

Deploying machine learning models in production carries its own unique set of challenges. Some challenges stem from different, and sometimes conflicting, objectives between analytics and production. Others arise from technological limitations, business requirements, and even regulatory needs.

In this talk, we will focus on the part of the problem surrounding the handover of models from analytics to production. This process has multiple facets, with tasks executed at different points in time and with different degrees of automation possible. To name a few: model packaging, inference reproducibility, establishing what needs to be deployed, and deployment-related actions.

We'll share some of our experiences and strategies to tackle these challenges. For example, how we tackle the topic of contracts, interfaces, and responsibilities between modeling and production. Or how the role of automation in the pre-deployment process ensures a smooth and efficient model transition from an analytics model store to something ready for production once a model is approved.

Whether you are a data scientist developing models, an operations specialist tasked with deploying them, or a product/project owner supervising the process, we aim to ignite engaging and fruitful discussions. For data scientists, to have a window into what happens after they are done with training a model. For operations specialists, to gain some strategies to improve their experience and success rate. And for a product owner, to get a framework on how to drive alignment.