Jeroen Overschie
Jeroen is a Machine Learning Engineer at Xebia Data (formerly GoDataDriven), in The Netherlands. Jeroen has a background in Software Engineering and Data Science and helps companies take their Machine Learning solutions into production.
Besides his usual work, Jeroen has been active in the Open Source community. Jeroen published several PyPi modules, npm modules, and has contributed to several large open source projects (Hydra from Facebook and Emberfire from Google). Jeroen also authored two chrome extensions, which are published on the web store.
Hope to see you at EuroPython 🇨🇿! 👋🏻
Sessions
What's this thing called MLOps? You may have heard about it by now, but never really understood what all the fuzz is about. Let's find out together!
In this tutorial, you will learn about MLOps and take your first steps in a hands-on way. To do so, we will be using Open Source tooling. We will be taking a simple example of Machine Learning use case and will gradually make it more ready for production 🚀.
We start with a simple time-series model in Python using scikit-learn and first add logging steps to make the performance of the model measurable. Don't worry: we will go through it step-by-step, so you won't be overwhelmed. Then, we will log our ML model and load it back into an inference step. Lastly, we will learn about deploying these actual models by Dockerizing our application 🙏.
What's this thing called MLOps? You may have heard about it by now, but never really understood what all the fuzz is about. Let's find out together!
In this tutorial, you will learn about MLOps and take your first steps in a hands-on way. To do so, we will be using Open Source tooling. We will be taking a simple example of Machine Learning use case and will gradually make it more ready for production 🚀.
We start with a simple time-series model in Python using scikit-learn and first add logging steps to make the performance of the model measurable. Don't worry: we will go through it step-by-step, so you won't be overwhelmed. Then, we will log our ML model and load it back into an inference step. Lastly, we will learn about deploying these actual models by Dockerizing our application 🙏.