What we’re about
Join Hamburg's largest data science & machine learning community! With around 4000 members and 10+ top-tier events a year, we dive into everything from databases to deep learning. Bring your passion and help shape the future of data in Hamburg!
Always reach out to me if you want to hold a talk or if you can offer to host the meetup at your company. This is a true community project in Hamburg. Without you this event wouldn't be that great!
Upcoming events (1)
See all- HH DS + ML Meetup at AdobeAdobe Systems Engineering GmbH, Hamburg
Welcome to the June version of our Data Science + Machine Learning Meetup Hamburg!
I'm super excited that thanks to Frederik Niesner we are able to host at the Adobe headquarter in Hamburg again.
We will have 2 talks - topics and speakers will be announced soon
Speakers:
- Eleftherios Mylonakis, Machine Learner, Mindpeak đź”— LinkedIn
Taming Gigapixel Images: Reproducible ML Workflows in Cancer Diagnostics
Handling complex, ever-growing datasets is a fundamental challenge in real-world machine learning. In cancer diagnostics, the scale is extreme: we deal with gigapixel images of human tissue, orders of magnitude larger than ImageNet. These aren’t just big; they’re mission-critical. Clinical-grade models require rigorous reproducibility, not just for scientific integrity but for patient safety.
In this session, you'll learn how Data Version Control (DVC) helps manage this complexity. Like Git for your data and experiments, DVC brings structure, reproducibility, and sanity to ML workflows.
P.S. This talk includes a dash of cooking to keep things spicy.- Helene Wittenberg, Computer Scientist, Adobe đź”— LinkedIn
The Last Mile of Machine Learning: Deploying Robust Models On-Device
This talk takes a look at an often overlooked segment of the machine learning model lifecycle: deploying and running ML models on user-controlled edge devices.
In just the past few years, we’ve seen a significant increase in deployment hardware. From CPUs to GPUs and dedicated NPUs, processors designed specifically for running ML models can now be found in almost every end-user device.
What are the core differences between these devices? How can ML engineering teams guarantee accuracy and performance when they have limited control over the setup of end-user devices? How can we minimize the impact of OS and driver updates on deployed ML models?
Using examples from core feature development in Adobe Premiere Pro, these are some of the topics we will cover in our talk on robust on-device deployment in the wild.Agenda:
- 18:00 - 18:15 Doors open, have a drink
- 18:15 - 18:30 Welcome Opening
- 18:30 - 19:15 First Talk
- 19:15 - 19:45 Break
- 19:45 - 20:15 Second Talk
- 20:15 ~ 21:30 Networkingđź“Ť Location: Adobe Hamburg
The Meetup language and all talks will be in English.
- Eleftherios Mylonakis, Machine Learner, Mindpeak đź”— LinkedIn
