MLOps Deutschland
MLOps: get models out of notebooks and into production
We help teams bring ML models from notebooks into stable production systems: reproducible, observable, and maintainable.
Typical problems we solve
Many ML projects stall in the POC stage because ownership, security, deployment, and observability are addressed too late.
- No reproducible training runs
- No clear production handoff
- Monitoring, drift, and rollback missing
Our MLOps stack
We deliberately pick maintainable components your team can run long-term instead of a heavyweight platform nobody adopts.
- MLflow, GitHub/GitLab CI, Docker, Kubernetes
- Triton, BentoML, SageMaker, or Vertex AI
- Prometheus/Grafana, Evidently, and runbooks
FAQ
Do we need Kubernetes for MLOps?
Not necessarily. We right-size the platform to your scale — managed serving or containers are often enough. We avoid complexity that nobody will maintain.
Can you set up monitoring for models already in production?
Yes. We add drift detection, performance monitoring, alerting, and rollback paths to existing deployments.
Make your ML production-ready
Book a call and we'll review your model lifecycle and propose a concrete MLOps roadmap.
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