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Social Event
Kickstart AI

E: fleur.prince@kickstart.ai

Language: English
Date: 10 Sep 2024
Time: 18:00 - 21:00
Entry fee: Free

Location:

AI Innovation Center

Register Now

This meetup is set to take place on September 10, at the AI Innovation Center in the High Tech Campus, Eindhoven. Mark your calendar and join us for an evening of insights, networking, and cutting-edge technology!

Learn the ins and outs of cutting-edge AI and machine learning with our expert speakers, Merel Theisen (Principal Software Engineer at QuantumBlack) and Emilio Oldenziel (Machine Learning Engineer at Eraneos). Discover how software engineering principles can elevate machine learning projects and how AI is optimizing rail traffic control through innovative solutions.

Come and connect with fellow AI experts at our Cutting-Edge AI and Machine Learning Innovations meetup, where you'll have the chance to learn from top experts in the field and share your own experiences. Our events provide a platform for collaboration and knowledge sharing, helping us all to advance in the fast-evolving world of AI. 🚀

And after an evening of learning, enjoy networking drinks and bites, where our speakers will be available to chat and answer any questions you have about AI and machine learning. 💬

Important Details:

Date: September 10

Time: 18:00 - 21:00

Location: AI Innovation Center - High Tech Campus, Eindhoven

Don't miss this opportunity to immerse yourself in the world of AI and machine learning. See you there!


 

Speaker 1: 


 

Merel Theisen - Principal Software Engineer at QuantumBlack


 


 

Title

Embedding Software Engineering Best Practices into Machine Learning Projects with Kedro

 

Abstract

In this talk, I will explore how software engineering best practices such as modularity, separation of concerns, testability, and reproducibility can elevate the quality and deployability of machine learning projects. Focusing on the Kedro framework, I’ll uncover how these principles integrate into data workflows, making complex projects more manageable and scalable. Attendees will gain practical insights into improving project design, ensuring code quality, and facilitating smoother transitions to production environments. No extensive software engineering background is required, making this an accessible and informative session for all data professionals looking to enhance their knowledge of software principles through Kedro.

 

Bio

I am a Principal Software Engineer at QuantumBlack, where I am currently the tech lead of Kedro, an open-source project part of the Linux Foundation. I have over eight years of experience in the software industry, with most of my career focused on backend product engineering. I am passionate about building products that solve real user problems, and I care deeply about creating robust, well-tested software that follows good engineering principles. I am also a strong advocate for open-source software, and I find working with the community to be both inspiring and energising.

 

Speaker 2: 


 

Emilio Oldenziel-  Machine Learning Engineer at Eraneos


 


 

Bio: Emilio is an expert in the field of machine learning. At the Eraneos Data & AI practice he is responsible for advising on, developing and implementing AI solutions. He has a background in computing science and has worked on AI projects for companies like Porsche, Deutsche Bahn, Enexis and HTM. Transport and Logistics is one of his industry focuses, where he sees a lot of value in applying AI.

Title: Optimizing Rail Traffic Control using a Digital Twin and Reinforcement Learning

Abstract: In this talk, I will do a deep dive into one of our recent customer cases. In the customer’s railway network, train frequency is high, with trains departing every 3 minutes at some stations. Consequently, even a small disruption can affect the punctuality of many subsequent trains. Train dispatchers are tasked with resolving conflicts efficiently to minimize delays. I will discuss how a Digital Twin and using Reinforcement Learning (RL) can support dispatchers in making smarter decisions and the challenges of implementing RL solutions. I will demonstrate how combining scientific and practical RL knowledge reduced delays by over 58,000 minutes annually.

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