AI & ML Research Events in Stockholm

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).

This page narrows the Stockholm calendar to AI & ML Research events. It's a subset of Sweden's wider tech-event schedule, useful when you want something specific to go to in the city this month.

Stockholm has one of Europe's strongest product, fintech, and games-industry event calendars. The city's concentration of consumer-facing scaleups (Klarna, Spotify, iZettle, King, Mojang) shows up in unusually strong product-management, growth, and data-product event content. Events that in other cities would be engineering-focused tilt toward product experimentation and metric-driven design here.

NOTE 1: This is part 2 of the lab. If you haven't attended the first lab, please
read the "New to the Lab" section below on how to prepare.
NOTE 2: Spots are limited. If something comes up, please give up your spot
so others can attend ❤️

LAB:
In this hands-on lab, we will work with the same basic setup, built around
two core components:
Model: XGBoost
Task: Predicting sales outcomes

Level
Basic knowledge of Python and some familiarity with machine learning or
statistical modeling is recommended.

XGBoost
XGBoost is a natural choice for this kind of project. It's powerful, flexible, well-documented and widely used in real-world forecasting tasks, yet still
accessible enough that you don't need deep domain expertise to get
started. The sales-prediction problem itself strikes a nice balance: realistic,
but not so specialized that it requires industry knowledge. We'll go through
the notebook and experiment with different settings to understand how
they affect the model. The code covers:

Data preprocessing
Exploratory analysis
Feature engineering
Handling missing values and outliers
Fine-tuning
Evaluation

Comparing results across different approaches can help us see how different settings affect model performance. The goal is not just to build
a model, but to learn from each other, compare methodologies, and deepen
our understanding of predictive modeling as a whole.

New to the Lab?
Once you've signed up, I'll send you code + data + a list of core concepts.

Make sure you can run the notebook without errors before the lab. If
you get errors, check that all required tools, libraries/packages are
installed.
Try to get an overview of the concepts and terms (read, google, or ask an AI). You don't need to have a solid grasp of the concepts and
terms but be aware that we won't be going through these during the
lab. Full focus on the code and optimizing the model.
The notebook is in Python, but an R draft is also available if you're more
comfortable with that.

☕ Since the event takes place at a café, it's expected that participants order something to eat or drink during the session 😇

All right!!!

Meetup #36: "Token effciency & Air-gapped MLOps" with Cloudera, Qdrant, evroc and SAAB will take place June 3 at AI Sweden. The program is now final! :-)

Event Program
- Doors open at 17:00 CET
- Talks begin at 17:45 CET
- There will be pizzas & drinks
- There may be a moderated Q&A session....

Speaker Line-Up
- Ewa Szyszka, DevRel Engineer at **Qdrant **will give a talk titled "Grow the Data, Shrink the Bill". Abstract: Agent efficiency through vector search. The core argument: AI agents waste tokens in four ways: context accumulation (stale tools, redundant reads), payload inefficiency (loading too much context), generative waste (restating, re-planning), and failure recovery (bad tool calls, self-correction loops). Stuffing the context window with everything works but is 50–100× more expensive than targeted retrieval. The alternative: use vector embeddings to increase information density, so an orchestrator works only with retrieved context from a vector store. You back this up with a benchmark experiment across three datasets (SWE-Bench, LongMemEval, FinanceBench) comparing agentic grep, Qdrant BM25 sparse, and Qdrant dense retrieval, measuring tokens-to-resolution as the primary metric.

- Markus Kjellner, Regional Vice President, Nordics & Baltics at Cloudera will give a talk titled "GenAI and MLOps without compromising on data sovereignty in strict air-gapped security environments". Abstract: Unlock the power of GenAI and MLOps without compromising on data sovereignty or strict air-gapped security. Discover how you can bring cutting-edge, cloud-native AI capabilities directly to your data within fully disconnected and sovereign environments.

- Peter Sundström, AI & Digitalisation Transformation Lead at SAAB will give a talk titled "MLOps for Electronic Warfare". Abstract: MLOps for electronic warfare enables rapid deployment and updating of ML models for RF signal detection and threat classification. Models often run on edge platforms with limited connectivity and strict latency requirements. The main challenge is keeping models reliable in dynamic and adversarial spectrum environments.

- Mikael Vesavuori, Product Manager AI Products, evroc will give a talk titled "AI Serving Across the Control Spectrum". Abstract: AI serving is shifting from simple cloud APIs toward a broader spectrum of deployment models: sovereign cloud, dedicated environments, on-prem, hybrid, and air-gapped AI." In this talk, Mikael Vesavuori from evroc explores when different AI serving models make sense, what cloud enables compared to isolated environments, and why the future may be less about choosing one model and more about controlling where each AI workload runs.

Looking forward to seeing you there,

/Patrick & the Stockholm MLOps Team

AI & ML Research Events in Stockholm

Upcoming AI & ML Research events in Stockholm

Part 2: Statistikklubben LAB - Sales Prediction with XGBoost

Token Efficiency & Air-Gapped MLOps

Stockholm MLOps Summerbash!!!