Glomax
All Services
MLOps

MLOps & AI Platform Engineering

The gap between a working prototype and a system your business depends on is mostly infrastructure. Reproducible training runs, a model registry with lineage, evaluation that runs before deployment rather than after the complaint, cost attribution per team, and someone who gets paged. We build that platform, or we fix the one you have, and we hand it over with documentation your team can actually operate from.

Discuss Your Project See What We Have Built
What We Deliver

Our Capabilities

Comprehensive MLOps capabilities backed by deep expertise and proven methodologies.

Training pipelines — reproducible, scheduled and versioned end to end
Model registry and lineage — which data, which code, which model, in production where
Evaluation in CI — quality gates that block a regression before it ships
Deployment patterns — shadow, canary and blue-green for models as well as services
Inference infrastructure — autoscaling, batching and GPU scheduling where it is warranted
Monitoring — drift, latency, error rates and quality signals in one place
Cost attribution and control — token and compute spend visible per team and per feature
Feature stores — one definition of a feature for training and serving
Experiment tracking — results that are findable six months later
Runbooks and on-call — what to do at 3am, written before 3am
Tech Stack

Technologies We Use

We leverage industry-leading tools and frameworks to build robust, scalable solutions.

Kubernetes
Docker
Terraform
MLflow
Kubeflow
Airflow
Dagster
Ray
AWS SageMaker
Vertex AI
Prometheus
Grafana
Langfuse
GitHub Actions
ArgoCD
Our view

What we would tell you in the first meeting

Nobody is promoted for building the platform, and every organisation that skipped it ends up rebuilding its models from scratch when the one person who understood them leaves. Pay for the boring layer early. It is considerably cheaper than the alternative.

FAQ

MLOps — Frequently Asked Questions

We already have models in production. Why would we need this?+

The question to ask is what happens when the person who built them leaves. If you cannot reproduce a training run, cannot tell which data version produced the model that is live, and have no evaluation running before deployment, then what you have is not production — it is a model that has not broken yet.

Is this worth it for a small number of models?+

A proportionate version is. You do not need a full platform for three models, but you do need reproducible training, a record of what is deployed where, and evaluation that runs before release. We build to the scale you are at, with room for the scale you are heading to.

Can you take over an existing setup rather than rebuild it?+

Usually, yes. We assess what you have, fix what is fragile, and migrate models onto a common footing rather than starting again. Rebuilds are occasionally the right answer, but they are the expensive answer and we will make the case before recommending one.

Explore More

Related Services

AI & ML

Machine learning that survives contact with production. We build models, the pipelines that feed them and the monitoring that tells you when they drift.

Learn More

Generative AI

LLM applications built like software: evaluated, versioned, cost-controlled and safe to put in front of customers.

Learn More

AI Agents

Agents that do the work — read the inbox, update the record, chase the approval — with the permissions, audit trail and stop button that makes that safe.

Learn More

Ready to Get Started with MLOps?

Tell us what you are trying to build and what is in your way. We will tell you how we would approach it — including when the answer is that you do not need us.

Talk to an engineer