SVC.001 · PIPELINES
Reproducible MLOps pipelines
AI-aware CI/CD, containerisation, model registry with version and dataset traceability, automatic rollback when quality drops.
CI/CDMLFLOWROLLBACK
AI model deployment and MLOps
We take your models to production and keep them in shape: CI/CD pipelines for AI, drift monitoring, continuous fine-tuning, GPU hosting on-premise or in the Swiss cloud. AI model deployment without the nasty surprises.
01 · Typical use casesFour pillars of MLOps.
Four pillars of MLOps.
An operational discipline.
A model that works in a notebook does not necessarily work in production. Reliable AI model deployment requires reproducible pipelines, rigorous monitoring and an update strategy — especially when the model drives real business decisions.
We build the MLOps infrastructure around your constraints: GPU budget, latency requirements, regulatory obligations, internal team skills. The goal is to make you autonomous on maintenance, not to create a permanent dependency.
SVC.002 · MONITORING
Quality monitoring
Automatic probes on outputs (LLM-as-judge), drift detection, inference cost and percentile latency tracking. Alerts on business thresholds.
LLM-JUDGEDRIFTALERTS
SVC.003 · GPU INFERENCE
Hosting & GPU inference
vLLM deployment on Kubernetes with autoscaling. GPU sizing (A10G, A100, H100) based on model, volume and your sovereignty constraints.
VLLMK8SGPU
SVC.004 · TAKEOVER
Existing deployment takeover
Technical audit of an existing infrastructure, identification of fragility points, stabilisation plan and upskilling of your team.
AUDITMIGRATIONDOC
02 · Our approachAudit, pipeline, observability.
Audit, pipeline, observability.
From notebook to production.
Monitoring an AI model goes far beyond server uptime. We instrument output quality, input data drift, inference cost and percentile latency. Alerts are configured on business thresholds — not just technical ones.
Step 01
Audit & instrumentation
Inventory of what's in place, log quality, fragilities identified. Probes are dropped in before any rebuild so we can measure what changes.
Step 02
Reproducible pipeline
CI/CD, model registry (MLflow, DVC), regression tests and canary deployments. Automatic rollback when degradation is detected.
Step 03
Continuous monitoring
LLM-as-judge on a reference set, drift tracking, cost per request, P95 latency. Alerts configured on business thresholds.
03 · Stack & technologies
Our reference MLOps stack.
// inference & orchestration
01
GPU inference engine
vLLM
Continuous batching, KV cache, maximum throughput on open-weights models.
02
Orchestration & autoscaling
Kubernetes
Autoscaling based on actual load, rolling deploys, workload isolation.
03
A10G · A100 · H100
NVIDIA GPU
Sizing based on model size, target throughput and budget.
// pipeline & monitoring
04
Registry & versioning
MLflow · DVC
Traceability of model versions, datasets and experiments.
05
Observability & alerts
Prometheus · Grafana
System and business metrics, dashboards, configurable alerts.
For on-premise deployments in Switzerland, we select and configure GPUs based on the model and request volume. For companies without dedicated servers, several referenced Swiss cloud providers enable a quick start without compromising data sovereignty.
04 · FAQ
Frequently asked questions.
05 · Go further
Related services.
- AI ApplicationsTurnkey AI application development, model fine-tuning, cloud or on-premise integration.
- Sovereign AIYour data stays in Switzerland. Your models run at your place. Open-weights LLM, on-premise GPU, nLPD compliance.
- Consulting & auditAudit, strategy, model selection, nLPD compliance — a clear direction for your AI initiatives.
Reply within 24 business hoursGot a use case in mind?
Got a use case in mind?
Let's talk.
Costed audit, measurable prototype, sovereign deployment. No sales middleman — you speak directly to a member of the technical team.
For companies based in Lausanne (Vaud), Geneva, Neuchâtel, Fribourg, Jura and Valais. Learn more about our AI agency.