AI Infrastructure, Orchestration & On-Prem AI | Stockholm MLOps Event #28 | February 27 2026
Insights from the Stockholm MLOps community
Stockholm MLOps #28 focused on a topic becoming increasingly important across enterprise AI:
On-prem MLOps and operational AI infrastructure.
Across the event, the discussions centered around:
Open Source AI stacks
orchestration
AI factories
infrastructure ownership
operational scalability
and secure AI deployment in regulated environments
The event reflected a broader shift happening across the industry:
AI is increasingly becoming an infrastructure and operational challenge — not just a model challenge.
Summary — Key Signals from the Event
Infrastructure ownership is becoming strategic
Open Source AI stacks are becoming foundational building blocks
AI orchestration is emerging as a core operational layer
Governance and trust are moving closer to infrastructure
Operational maturity increasingly determines AI success
Speakers & Companies Featured at Stockholm MLOps #28
Andreas Bergqvist — AI Specialist Sales EMEA, Red Hat
Andreas Bergqvist presented Red Hat’s approach to Open Source AI infrastructure and operational AI systems.
The session focused on:
modular AI building blocks
orchestration
trust and security
and operational AI deployment from day one
The talk strongly reflected how Open Source infrastructure is increasingly becoming part of enterprise AI strategy.
Leif Nordlund — AI Specialist, Lenovo
Leif Nordlund focused on practical infrastructure considerations for building AI systems on-prem.
The session covered:
GPU infrastructure
scaling from compact systems to large GPU environments
operational infrastructure planning
and lessons learned from AI infrastructure deployments over the past decade
The discussion highlighted how infrastructure planning increasingly shapes AI strategy itself.
Simon Janeck — Head of R&D and AI, Aixia
Simon Janeck focused on operational AI delivery and AI factory concepts.
The session reflected the growing need for:
deployment consistency
operational support
orchestration
infrastructure management
and production-ready AI systems
The “AI Factory” framing highlighted the shift from isolated PoCs toward operational AI environments.
Johan Lennartson — AI Strategist, Sogeti
Johan Lennartson shared lessons from supporting a large government agency on its AI journey.
The discussion focused on:
secure AI deployment in the public sector
governance
hardware constraints
innovation management
and long-term operational value creation
The session highlighted how regulated environments require very different operational AI decisions compared to startup-style deployments.
Key Operational Signals from the Event
1. Infrastructure Ownership Is Becoming Strategic
Several sessions reflected a growing move toward infrastructure ownership and operational control.
Organizations increasingly want:
governance
portability
operational flexibility
and infrastructure independence
Infrastructure decisions increasingly shape AI capability itself.
2. Open Source AI Is Becoming Operational Infrastructure
The event strongly reflected growing interest in Open Source AI stacks and modular infrastructure.
The focus increasingly shifts toward:
interoperability
flexibility
orchestration
and avoiding long-term lock-in
Open Source is increasingly viewed as operational infrastructure rather than experimentation tooling.
3. AI Orchestration Is Becoming a Core Engineering Layer
As organizations move beyond isolated AI experiments, orchestration increasingly becomes necessary for:
deployment consistency
lifecycle management
operational scaling
workload coordination
and infrastructure governance
AI systems increasingly resemble platform engineering systems.
4. AI Factories Require Operational Discipline
The “AI Factory” framing reflected a broader shift toward repeatable operational AI systems.
Scaling AI successfully increasingly requires:
deployment standards
observability
operational support
governance
and infrastructure planning
Operational maturity increasingly becomes the differentiator between experimentation and production.
5. Governance and Security Are Moving Into Infrastructure
Security and trust repeatedly appeared as infrastructure concerns rather than downstream policy discussions.
This increasingly affects:
deployment architecture
orchestration
runtime operations
access control
and operational governance
Governance increasingly becomes part of the runtime environment itself.
6. Hardware Constraints Continue to Shape AI Strategy
The event repeatedly highlighted how infrastructure limitations continue to shape operational AI decisions.
GPU allocation, hardware availability, and infrastructure planning increasingly affect:
scaling strategies
deployment decisions
and operational priorities
Infrastructure availability may increasingly become more important than model capability alone.
Tensions Emerging from the Event
Cloud vs On-Prem
Organizations increasingly want cloud flexibility while maintaining operational control.
Sovereignty vs Convenience
Managed AI services simplify operations, while on-prem infrastructure provides greater ownership and governance.
Portability vs Optimization
Modular systems improve flexibility, while optimized deployments often depend on tightly integrated environments.
Innovation Speed vs Governance
Governance requirements can slow experimentation unless integrated directly into operational workflows.
Scaling Ambition vs Operational Readiness
Many organizations want enterprise-scale AI before operational processes are mature enough to support it.
Why These Signals Matter
Stockholm MLOps #28 reflected a broader industry shift:
AI is increasingly becoming infrastructure.
The next phase of MLOps increasingly revolves around:
operational scalability
orchestration
governance
runtime reliability
infrastructure ownership
and long-term operational management
The event highlighted the growing convergence between:
AI engineering
infrastructure strategy
platform engineering
and enterprise operations
Related Insights
- Sovereign AI in Production | Stockholm MLOps #31
- Optimizing Inference | Stockholm MLOps #29
- AI Infrastructure & Operational Scale | Stockholm MLOps #27
Join the Community
👉 Explore all events: https://www.meetup.com/stockholm-mlops-community/
👉 Explore this event: On-Prem MLOps with Lenovo & Red Hat #28
Event Details
Location: AI Sweden, Stockholm
Date: February 26, 2026
Companies represented: Red Hat, Lenovo, Aixia, Sogeti
Topics: On-Prem AI, AI infrastructure, orchestration, Open Source AI, operational MLOps
Event: Stockholm MLOps #28