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ML Engineering & MLOps

Models are easy to train and hard to keep alive. We build the engineering layer that takes a notebook to a system your team can trust at 3 a.m.

Where we help​

Deployment​

Package models as versioned, observable services — batch, real-time, or streaming. Kubernetes, serverless, or on-prem, matched to your stack and data-residency needs.

Pipelines​

Reproducible training and inference pipelines with data versioning, CI/CD, and automated retraining triggered by drift or schedule.

Monitoring​

Track data drift, model quality, latency, and cost in production. Alerts that page a human before customers notice.

Platform​

A paved road for your data scientists: feature store, model registry, and templates so the next model ships in days, not quarters.

What good looks like​

# Every model we ship is versioned, gated, and observable.
deploy:
  model: churn-predictor
  version: 2.4.1
  gates:
    - eval_auc: ">= 0.86"      # block regressions
    - latency_p99_ms: "< 150"
  monitor:
    drift: psi                  # population stability index
    alert_channel: slack#ml-ops
  region: eu-central-1          # data stays in the EU

Typical outcomes​

  • Deploy time from weeks to a single merge.
  • Drift caught and retrained automatically, not via customer complaints.
  • A platform your own engineers extend without us.

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