ML Engineering & MLOps

Productionize models with reliable pipelines, deployment, and monitoring — so your AI keeps working after launch.

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

Where I 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 I 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 me.

Next: AI Strategy · Talk to me