Join us for an exciting Code Breakfast "How to MLOps: Experiment tracking & deployment." on September 27th.
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 use Open Source tooling. By taking a simple example of a Machine Learning use case, we will explore how to gradually make it ready for production 🚀.
Starting with a simple time-series model in Python, using scikit-learn we 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 and subsequently running our container on Azure or GCP 🙏
- What and why of MLOps
- Experiment tracking
- Demo: setup & experiment tracking
- Hands-on: setup & experiment tracking
- Serving the model
- Containerizing the application
- Demo: serving & deployment
- Hands-on: serving & deployment
- Wednesday, September 27th
- 8:30 am walk in + breakfast
9:00 - 11:30 Code Breakfast
- Xebia Data office, Wibautstraat 202, Amsterdam
- Seats are limited, so please register to attend.
Jeroen Overschie 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.
Yke Rusticus 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.
We can't wait to see you there!