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Request the NDSML Summit 2026 PDF Agenda

11th edition – A decade of shared knowledge, community, and ambition

The NDSML Summit is an annual event bringing together the Data Science and ML community in the Nordics to share ideas and discuss ways to harness the full potential of traditional ML models, cutting-edge generative AI systems, and the emerging class of Agentic AI as well as explore the development and the maturity of Data Science and AI as a function.

With 30+ international speakers, interactive panels, and unmatched networking, it’s the go-to event for practitioners working with Data Science, DataOps, ML, AI, Gen AI, Deep Learning, MLOps, LLMOps, AI Product and Data Engineering.

The 11th edition is limited to just 400 onsite participants to ensure an exclusive and impactful experience. – Request the agenda today to explore more about the summit.

Our 2026 Speakers

Arek Borucki, Machine Learning Platform & Database Engineer at Hugging Face, speaker at NDSML Summit 2026.
Arek Borucki

Machine Learning Platform & Database Engineer
Hugging Face

Eugene Klyuchnikov, Principal Data Engineer at Zalando, speaker at NDSML Summit 2026.
Eugene Klyuchnikov

Principal Data Engineer
Zalando

Luba Weissmann, AI & Data Director at Epiroc, speaker at NDSML Summit 2026.
Luba Weissmann

AI&Data Director
Epiroc

Joachim Stegmann, Director AI & ML at Deutsche Telekom AG, speaker at NDSML Summit 2026.
Joachim Stegmann

Director AI&ML
Deutsche Telekom AG

Andrea Kovalova, Data Scientist at Wärtsilä, speaker at NDSML Summit 2026.
Filipa Peleja

Director Data & AI Operations
The LEGO Group

Nishant Gupta, Staff Software Engineer at META, speaker at NDSML 2026
Nishant Gupta

Staff Software Engineer
Meta

Peter Malmberg, VP Innovation & Digital Divisional Management at Epiroc, speaker at NDSML Summit 2026.
Peter Malmberg

VP Innovation & Digital Divisional Management
Epiroc

Maria Litova, Data Scientist at Wärtsilä, speaker at NDSML Summit 2026.
Maria Litova

Data Scientist
Wärtsilä

Valentin Buchner, Chief Architect at Chapter Two, speaker at NDSML Summit 2026.
Valentin Buchner

Chief Architect
Chapter Two

David Walléni, CEO & Co-founder at Intric, speaker at NDSML 2026
David Wallén

CEO & Co-founder
Intric

Fredrik Urby, Head of Data Science at Länsförsäkringar AB, speaker at NDSML 2026
Fredrik Urby

Head of Data Science
Länsförsäkringar AB

William Smedberg, AI Lead at Sandvik, speaker at NDSML Summit 2026.
William Smedberg

AI Lead
Sandvik

Mukrram Ur Rahman, Principal Software Engineer at Zalando, speaker at NDSML 2026
Mukrram Ur Rahman

Principal Software Engineer
Zalando

Aurora van de Walle

AI Engineer
ASSA ABLOY

Vilhelm von Ehrenheim

Co-founder & CAIO
QA.tech

Petra Dalunde

Head of AI Ecosystem & Policy Sweden AI Factory
RISE – Research Institutes of Sweden

Jonas Alm

Head of Technology and Data
Einride AB

Andrea Kovalova, Data Scientist at Wärtsilä, speaker at NDSML Summit 2026.
Andrea Kovalova

Data Scientist
Wärtsilä

Mukrram Ur Rahman, Principal Software Engineer at Zalando, speaker at NDSML 2026
Nicolay Nerdal

Data Scientist
SpareBank 1 SMN

Mukrram Ur Rahman, Principal Software Engineer at Zalando, speaker at NDSML 2026
Anton Smith

Product Director, Cloud Native AI Services
Nebius

Mukrram Ur Rahman, Principal Software Engineer at Zalando, speaker at NDSML 2026
Senadin Alisic

Strategy Planning
Combitech/Saab

Mukrram Ur Rahman, Principal Software Engineer at Zalando, speaker at NDSML 2026
Prayson Wilfred Daniel

Principal Data Scientist
Norlys

Mukrram Ur Rahman, Principal Software Engineer at Zalando, speaker at NDSML 2026
Love Regefalk

Senior Product Manager, AI
Vend

BE THE FIRST TO DISCOVER WHAT’S COMING NEXT

Discover the Unmissable Opportunities at the
NDSML Summit

Join us at the highly anticipated NDSML Summit and unlock a world of possibilities in the realms of Data Science, Machine Learning, Data Engineering, AI, GenAI, and much more.

By attending this event, you’ll gain exclusive access to:

Production-Ready AI Systems

Learn how enterprises are moving beyond experimentation toward deploying reliable, scalable, and operational AI systems across real business environments. Discover practical strategies for building AI applications, intelligent workflows, and autonomous systems that organizations can trust in production.

Autonomous Agents and Intelligent Workflows

Explore how enterprises are designing, orchestrating, and operationalizing AI agents and intelligent workflows capable of automating tasks, supporting decisions, and interacting across enterprise systems and business operations.

Enterprise AI Engineering

Gain practical insights into modern AI engineering practices including LLMOps, MLOps, orchestration, evaluation, deployment, observability, and operational AI architectures required to scale enterprise AI successfully.

Real World Enterprise AI Case Studies

Learn from organizations actively deploying Generative AI, AI agents, and intelligent systems into production. Discover practical implementation lessons, operational challenges, and measurable business outcomes.

AI Governance and Operational Trust

Understand how enterprises are balancing innovation, governance, security, compliance, observability, and human oversight while operationalizing AI across critical workflows and business functions.

Building Reliable AI Systems

Discover how organizations are addressing hallucinations, context management, reliability failures, evaluation, monitoring, and operational observability to create trustworthy enterprise AI systems.

AI Infrastructure at Scale

Explore the infrastructure, compute environments, vector databases, pipelines, streaming architectures, and AI platforms required to support production ready AI systems and enterprise scale intelligent operations.

AI Ready Data Engineering

Learn how modern data engineering practices are evolving to support autonomous AI systems through real time pipelines, contextual architectures, interoperability, metadata, and scalable AI ready data foundations.

Operationalizing Generative AI

Explore how organizations are transforming Generative AI from isolated copilots into integrated enterprise systems connected to tools, workflows, operations, and intelligent execution layers.

AI Evaluation and Observability

Discover how leading organizations continuously evaluate, monitor, observe, and improve AI systems in production environments through operational metrics, testing frameworks, telemetry, and reliability engineering.

AI Infrastructure Economics

Learn how enterprises are optimizing GPU utilization, inference costs, compute performance, scalability, and infrastructure efficiency while preparing for the next generation of enterprise AI workloads.

Production-Ready AI Systems

Learn how enterprises are moving beyond experimentation toward deploying reliable, scalable, and operational AI systems across real business environments. Discover practical strategies for building AI applications, intelligent workflows, and autonomous systems that organizations can trust in production.

Autonomous Agents and Intelligent Workflows

Explore how enterprises are designing, orchestrating, and operationalizing AI agents and intelligent workflows capable of automating tasks, supporting decisions, and interacting across enterprise systems and business operations.

Enterprise AI Engineering

Gain practical insights into modern AI engineering practices including LLMOps, MLOps, orchestration, evaluation, deployment, observability, and operational AI architectures required to scale enterprise AI successfully.

Real World Enterprise AI Case Studies

Learn from organizations actively deploying Generative AI, AI agents, and intelligent systems into production. Discover practical implementation lessons, operational challenges, and measurable business outcomes.

AI Governance and Operational Trust

Understand how enterprises are balancing innovation, governance, security, compliance, observability, and human oversight while operationalizing AI across critical workflows and business functions.

Building Reliable AI Systems

Discover how organizations are addressing hallucinations, context management, reliability failures, evaluation, monitoring, and operational observability to create trustworthy enterprise AI systems.

AI Infrastructure at Scale

Explore the infrastructure, compute environments, vector databases, pipelines, streaming architectures, and AI platforms required to support production ready AI systems and enterprise scale intelligent operations.

AI Ready Data Engineering

Learn how modern data engineering practices are evolving to support autonomous AI systems through real time pipelines, contextual architectures, interoperability, metadata, and scalable AI ready data foundations.

Operationalizing Generative AI

Explore how organizations are transforming Generative AI from isolated copilots into integrated enterprise systems connected to tools, workflows, operations, and intelligent execution layers.

AI Evaluation and Observability

Discover how leading organizations continuously evaluate, monitor, observe, and improve AI systems in production environments through operational metrics, testing frameworks, telemetry, and reliability engineering.

AI Infrastructure Economics

Learn how enterprises are optimizing GPU utilization, inference costs, compute performance, scalability, and infrastructure efficiency while preparing for the next generation of enterprise AI workloads.

Request the NDSML Summit 2026 PDF Agenda

11th Jubilee edition – A decade of shared knowledge, community, and ambition