📢 Devoxx Try-out: Maintaining Maven at scale & Machine Learning in Java

Did you miss the Devoxx tickets?

Do you want some extra time during Devoxx by pre-watching two talks?

This is your opportunity to watch two amazing speakers present their Devoxx talk before the event, let's call it a: Devoxx Try-out!

This time our co-host is Lunatech. They are an organization with self-managing developers in small teams. They work on projects for customers, when possible from the customer's location or from one of their own offices. Lunatech has offices in Amsterdam, Paris, Brussels, Newcastle and of course (our host) in Rotterdam.

📅 - Schedule - 📅

17:00: Doors are open, food and drinks, welcome!

18:30: Maintaining Maven at scale (Tim te Beek)

Twenty years of history, over ninety repositories, maintained by volunteers, yet depended on by an entire ecosystem. That’s the Apache Maven project. This project sure has interesting challenges when it comes to development, and making the best use of volunteers’ time. New features and fixes might take priority over less glamorous clean up, yet all are needed to keep the project maintainable for the future.

In this talk we will look at how we apply improvements to Apache Maven at scale. No one in their right mind would volunteer to make these changes by hand. But by using OpenRewrite and the Moderne platform, we can both leverage existing improvement recipes, as well as develop custom recipes for Apache Maven. We will outline some of the challenges, what’s already been done, what’s coming up next, and how you can help.

Walk away with a little more appreciation for Apache Maven, a look behind the scenes of how it’s continuously improving, and how you can contribute, whether that’d be for Apache Maven, or any other community with similar challenges.

19:30: Lessons learned using Machine Learning in Java (Jago de Vreede)

In this talk, I will share my journey of using machine learning in Java to build a visual recognition system that can identify Lego blocks. As a Java developer, I wanted to use Java for this project rather than Python, which is more commonly used for machine learning projects. I will explain the basics of machine learning and give an overview of the current Java libraries for machine learning and transferring pre-trained models. I will demonstrate how to train and modify existing models using transfer learning. The goal of the project is to create a Java solution that can identify the top 1000 most popular Lego bricks. I will explain all of this without using any complex mathematical formulas, making it accessible to those with no prior knowledge of machine learning.

20:30: Networking with some drinks

21:30: Time to go home.

More about our amazing guest speakers:

👨‍💻 Tim te Beek is a staff software engineer at Moderne, which automates software refactoring at scale. He has extensive experience contributing to and presenting on Open Source software within the Java ecosystem. Previously he worked as a consultant specializing in migration engineering and developer productivity.

👨‍💻 Jago de Vreede is a full-stack software engineer at OpenValue, as a software engineer he has seen a broad-spectrum of projects. He also likes to take Java to the extreme and do things which are normally not done with Java, like playing an automated guitar. He is passionate about his work and likes to share his knowledge and has therefore spoken at conferences worldwide.

🏢 Location:

Lunatech has generously offered to host this event in their Rotterdam office; you can find it here:

Hofplein 20 (on the 22nd floor)
3032 AC
Rotterdam

Lunatech is located on the 22nd floor in the Spaces building.

Please use the backdoor to enter after 18:00h (side to the parking entrance). Our office number is 010 750 2600. Please feel free to call us if the door is locked!

Final note:

Do you want to be a Rotterdam JUG (future Devoxx) speaker? We're always looking for speakers from our area!

Or perhaps you have an office and you'd like to host a meetup, drop us a message or talk to the organizers at the event!