MLOps · EonTech

The model is the easy part.
Keeping it reliable is the work.

We take models out of notebooks and run them in production. Deployment, CI/CD, drift monitoring, and governance, owned by the same senior engineers. Your models, your cloud, your IP.

Put a model in production Explore AI & ML
  • CanarySafe rollouts, fast rollback
  • DriftCaught before users notice
  • LineageEvery result reproducible

Why MLOps matters

Most ML never makes it to production

A model that scores well offline can fail the moment it meets live data. Inputs shift, features go stale, and accuracy erodes without a clear signal.

MLOps is the engineering that closes that gap. We make models deployable, observable, and reproducible, so the value you proved in a prototype survives contact with the real world.

See the data foundation underneath

What we operate

The full production loop for ML

We cover the parts that decide whether a model stays useful. Each one is built to be operated by your own team once we hand it over.

  • Model deployment

    Containerised serving with rollback and canary releases. A bad model never reaches all of your users at once.

  • CI/CD for ML

    Pipelines that test data, code, and the model together. Every promotion to production is reproducible and reviewed.

  • Monitoring & drift

    We watch inputs, predictions, and outcomes. Drift and degradation trigger alerts before metrics quietly slide.

  • Feature stores

    One consistent feature definition for training and serving. The skew that breaks live accuracy is designed out.

  • Reproducibility

    Versioned data, code, and weights. Any result can be rebuilt months later, which matters for audits and trust.

  • Scaling inference

    Autoscaling, batching, and cost controls. Latency targets hold under load without an open-ended cloud bill.

From notebook to service

Package, promote, monitor, retrain

A continuous loop, not a one-off deploy. The model improves on a schedule you control, with a full record of every change.

  1. 01

    Package

    We wrap the model with versioned dependencies and clear contracts. The notebook becomes a service with tests.

  2. 02

    Promote

    Automated pipelines validate, stage, and release. Canary first, full rollout once the signals hold.

  3. 03

    Monitor

    Live dashboards track drift, latency, and outcomes. Thresholds are set with you, not guessed at.

  4. 04

    Retrain

    Triggers and pipelines refresh the model on new data. Lineage records exactly what changed and why.

Not sure where ML fits yet? Start with AI strategy & consulting

Common questions

What teams ask before going live

  • We have a model that works in a notebook. Now what?

    That is exactly where MLOps starts. We package it as a tested, versioned service, wire up CI/CD, and put monitoring around it so it stays accurate once real traffic and real data hit it.

  • How do you catch a model going stale?

    We monitor input distributions, prediction patterns, and downstream outcomes. When drift crosses a threshold you agreed to, the system alerts and, where appropriate, kicks off a retraining pipeline.

  • Can you work with our existing cloud and tooling?

    Yes. We deploy into your AWS, GCP, or Azure account and fit your stack rather than imposing ours. Everything is infrastructure as code, so your team can operate it after handover.

  • Do we own the models and the pipelines?

    Completely. The model weights, the training code, the serving infrastructure, and the configuration are all yours. We build to avoid lock-in, never to trap you on a platform.

Make your models production-grade

Tell us what you have built and where it needs to run. We will scope the deployment, monitoring, and retraining loop, then ship it.

Put a model in production [email protected]