AI & ML Research Events in Europe
Europe's AI and ML research scene is a long tail of university labs, community research groups, and practitioner-run meetups that together punch well above their weight globally. This page covers the research end of the AI/ML event calendar: deep-learning meetups, computer vision and NLP groups, paper-reading clubs, and research-oriented conferences. It's aimed at ML engineers, applied researchers, and PhD students who care about how the models work, rather than how to integrate them into a product (that's the Applied AI category).
Anchor events include the MLcon series (Berlin, Munich, Amsterdam, London), PyData conferences, and regional deep-learning meetups like the Vienna Deep Learning Meetup. Quantum AI and quantum-computing events sit here too, since the European quantum community is research-heavy and overlaps meaningfully with the ML research crowd. Topics cover training and serving frameworks, fine-tuning technique, evaluation, quantization, model architectures, and the infrastructure that makes experimentation tractable.
Brainberg aggregates these into a single chronological European view. For the deluge of "how to ship a feature with an LLM" events, see the Applied AI category instead.
Upcoming events
MLcon Munich
Munich, 🇩🇪 Germany
LLMday: In-Person Event on LLMs, AI & ML | LISBON 2026
Lisbon, 🇵🇹 Portugal
LLMday: In-Person Event on LLMs, AI & ML in LISBON
Conference focused on Large Language Models (LLMs), Artificial Intelligence, and Machine Learning, bringing together engineers, researchers, and product teams to share real-world experience building, deploying, and operating AI systems.
🫆CFP
➡️SPEAKERS & TOPICS
🎟️ Important: FREE ENTRANCE. Meetup RSVPs are for interest only. Still NEED registration, it is handled exclusively through Luma here
AI, ML, and Computer Vision Meetup
Artificial Intelligence (AI) meetup Online
Temporal Graph Networks for Deep Learning on Dynamic Graphs
Ciaran will be presenting Temporal Graph Networks for Deep Learning on Dynamic Graphs by Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, Michael Bronstein.
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We discuss a different research paper every week. We post each week's paper in our GitHub repo - please read it before the meetup.
All events will be hosted on a Google Meet video call. Occasionally we also host an in-person event in our London office - watch this space for updates.
All recorded presentations can be found in our YouTube channel (don't forget to subscribe!).
Join our Discord channel: https://discord.gg/a2fMv7YAfs
Best of CVPR
Tech meetup Online
Azure Machine Learning Step 5: Deploying & Operating Models
In this fifth session of the Azure Machine Learning series, we’ll take the next critical step in the ML lifecycle: moving trained models into production and keeping them running reliably. Azure Machine Learning provides robust tools for deploying models as scalable endpoints, managing versions, and monitoring performance in real-world environments.
This session focuses on the practical side of deployment and operations (MLOps) within Azure ML. You’ll learn how to take a trained and registered model and turn it into a production-ready service, while also understanding how to manage, monitor, and update that service over time. Whether you’re continuing from Step 4 or already familiar with model training, this session will help you bridge the gap between experimentation and real-world impact.
You’ll learn:
- How deployment fits into the machine learning lifecycle
- Options for deploying models in Azure ML (real-time endpoints, batch endpoints)
- How to create and manage online endpoints using the Studio UI, SDK, and CLI
- How to package models with environments, scoring scripts, and dependencies
- Techniques for scaling, versioning, and updating deployments (blue/green strategies)
- How to monitor model performance, logs, and resource usage
- Best practices for reliability, cost optimization, and governance
- How to integrate deployed models into applications and workflows
This session is designed to help you move from “I have a trained model” to “I can deploy and operate it in production with confidence.” If you’re ready to deliver real value from your machine learning solutions and ensure they perform reliably at scale, this is your next step.
AI, ML, and Computer Vision Meetup
Artificial Intelligence (AI) meetup Online
London AI. ML and Computer Vision Meetup
London, 🇬🇧 United Kingdom
Artificial Intelligence (AI) meetup in London, United Kingdom
Zurich AI, ML and Computer Vision Meetup
Zurich, 🇨🇠Switzerland
Artificial Intelligence (AI) meetup in Zurich, Switzerland
GPU, CUDA, and PyTorch Performance Optimizations
Zoom link: https://us02web.zoom.us/j/82308186562
Talk #0: Introductions and Meetup Updates
by Chris Fregly and Antje Barth
Talk #1: GPU, PyTorch, and CUDA Performance Optimizations
Talk #2: GPU, PyTorch, and CUDA Performance Optimizations
Zoom link: https://us02web.zoom.us/j/82308186562
Related Links
Github Repo: http://github.com/cfregly/ai-performance-engineering/
O'Reilly Book: https://www.amazon.com/Systems-Performance-Engineering-Optimizing-Algorithms/dp/B0F47689K8/
YouTube: https://www.youtube.com/@AIPerformanceEngineering
Generative AI Free Course on DeepLearning.ai: https://bit.ly/gllm
AI, ML, and Computer Vision Meetup
Artificial Intelligence (AI) meetup Online