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MLOps: bringing engineering discipline to machine learning
Building a machine learning model is the easy part. Getting it into production reliably — and keeping it accurate and available over time — requires the same engineering discipline as any other critical software system. That is what MLOps delivers, and that is what IS Nordic builds for you.
We design and operate ML infrastructure that bridges the gap between your data science teams and your production environment, applying DevOps and platform engineering principles to the full ML lifecycle.
Business advantage
- →Models in production, generating business value — not stuck in notebooks
- →Consistent, reproducible deployments — no manual handoffs between teams
- →Model drift detected early — accuracy maintained over time
- →Regulatory-ready audit trails for AI/ML decision systems
Technical advantage
- →Automated training pipelines with experiment tracking
- →Versioned models with rollback capability
- →Inference serving at scale — low latency, high availability
- →Infrastructure as Code for all ML components — reproducible and auditable
- →EU-sovereign ML infrastructure — data stays in Denmark
Model Lifecycle Management
A model that cannot be reliably retrained, versioned, and monitored is a liability. IS Nordic designs end-to-end ML pipelines that manage the full lifecycle of your models — from initial training runs through production deployment to ongoing monitoring and retraining.
Model drift is inevitable: the world changes, your data distribution shifts, and yesterday's accurate model becomes tomorrow's liability. We build monitoring and retraining pipelines that catch drift early and maintain model quality without requiring manual intervention.
Training Pipeline Automation
Automated, reproducible training pipelines with experiment tracking. Every training run is logged — parameters, metrics, data versions — so you can compare runs, audit decisions, and reproduce any model exactly.
Model Registry & Versioning
Versioned model artefacts with promotion workflows from development through staging to production. Roll back to a previous version in minutes if a deployment degrades performance.
Production Monitoring & Drift Detection
Live monitoring of model prediction quality, data distribution, and infrastructure health. Automated alerts when drift thresholds are exceeded, with retraining triggers configured to match your business requirements.
ML Infrastructure
Machine learning workloads have specific infrastructure requirements — high-memory training nodes, GPU acceleration, fast storage for large datasets, and low-latency inference endpoints. IS Nordic designs and operates ML infrastructure matched to your workload profile, whether on-premises, cloud, or hybrid.
GPU & Accelerated Compute
Training and inference infrastructure with GPU support, deployed on Kubernetes with proper resource scheduling. We configure node selectors, resource limits, and fractional GPU allocation to maximise hardware utilisation.
Scalable Storage for ML
High-throughput object storage for training datasets and model artefacts, integrated with your pipelines. Data versioning, lineage tracking, and access controls applied to all ML data assets.
Inference Serving at Scale
Production inference endpoints built on Kubernetes — auto-scaling, load balanced, and monitored. We select and configure the right serving framework (Triton, TorchServe, custom APIs) for your model type and latency requirements.
EU-Sovereign ML
All ML infrastructure hosted in Danish datacenters. Training data and model artefacts remain under EU legal jurisdiction — critical for organisations handling personal data or operating under GDPR, NIS2, or sector-specific AI regulations.
CI/CD Pipeline Integration
ML systems do not exist in isolation. IS Nordic integrates your ML pipelines with the software delivery processes already in place — version control, CI/CD, testing frameworks, and deployment tooling — so that model updates follow the same engineering standards as application code.
GitOps for ML
Model configurations, training parameters, and deployment specifications managed in Git. All changes go through pull requests — reviewed, auditable, and reversible. The same GitOps workflow that governs your application code governs your ML components.
Automated Testing for ML
Quality gates built into the pipeline: data validation, model evaluation against holdout sets, performance regression checks, and shadow deployment tests before live traffic exposure.
Integration with Existing Tooling
IS Nordic integrates ML pipelines with the tools your teams already use — GitHub Actions, Azure DevOps, Jenkins, ArgoCD. We extend your existing CI/CD investment rather than replacing it.
Let's talk about your MLOps needs
Whether you are deploying your first model to production or scaling an existing ML platform, IS Nordic brings the infrastructure expertise and engineering discipline to make it reliable, auditable, and sovereign.
Phone: +45 7026 2500 | Email: info@isnordic.dk