Yke Rusticus
Yke is a Machine Learning Engineer at Xebia with a background in astronomy and artificial intelligence. In the industry, he learned that models and algorithms often do not get past the experimentation phase, leading him to specialise in MLOps to bridge the gap between experimentation and production. As a professional in this field, Yke has developed ML platforms and use cases across different cloud providers, and is passionate about sharing his knowledge through tutorials and trainings.
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 🙏.