From Notebook to Production

Your data scientists built great models. Now you need them in production—reliable, scalable, and maintainable. That's where most teams get stuck. We unstick them.

Our MLOps Services

  • Model Deployment — Transform notebooks into production APIs with proper error handling, logging, and scaling
  • ML Pipelines — Automated training, validation, and deployment pipelines that your team can maintain
  • Monitoring & Alerting — Track model performance, detect drift, and alert before problems hit users
  • Infrastructure Setup — Right-sized compute, cost-optimized architecture, security best practices

Common MLOps Challenges We Solve

Model Performance Degradation

Models that worked great in testing slowly get worse in production. We set up monitoring to catch drift early and automated retraining to keep accuracy high.

Scaling Bottlenecks

Your ML service can't keep up with traffic. We optimize inference, add caching where appropriate, and design architectures that scale cost-effectively.

Deployment Fear

Your team is afraid to deploy model updates because things break. We build CI/CD pipelines with automated testing, canary deployments, and easy rollback.

Technical Debt

Your ML code is a mess of notebooks and scripts nobody understands. We refactor into maintainable, documented systems your team can actually work with.

MLOps Stack We Work With

  • Experiment Tracking: MLflow, Weights & Biases, Neptune
  • Orchestration: Airflow, Prefect, Dagster, Kubeflow
  • Model Serving: FastAPI, TorchServe, TensorFlow Serving, Triton
  • Infrastructure: Docker, Kubernetes, AWS SageMaker, GCP Vertex AI
  • Monitoring: Prometheus, Grafana, custom dashboards

Engagement Models

MLOps Audit

We assess your current ML infrastructure and provide a prioritized roadmap for improvement. 1-2 weeks, €5,000-€10,000.

Production Deployment

Take a specific model from notebook to production. 2-4 weeks, €15,000-€30,000.

MLOps Infrastructure Build

Full MLOps stack setup for your organization. 4-8 weeks, €30,000-€60,000.

FAQ

Do we need MLOps if we only have a few models?

Even one model in production needs monitoring and a deployment process. Start simple—we can set up lightweight MLOps that grows with you.

Our data scientists say they can handle deployment themselves.

They probably can build something. The question is: can they build something reliable that doesn't become a maintenance nightmare? We've seen many teams burn months on production issues that proper MLOps would prevent.

How do you handle our existing infrastructure?

We work with what you have. If you're on AWS, we'll use AWS tools. If you have existing Kubernetes, we'll deploy there. No rip-and-replace unless it's truly necessary.

Get Your Models Into Production

Tell us about your ML deployment challenges. We'll suggest the right approach for your situation.

We typically respond within 24 hours. No spam, no sales pitch.