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MLOps Services
in Doha.

SM Stratagem builds mlops services for Doha teams that care about deployment, evaluation, and monitoring — not just the demo that impresses the boardroom.

DohaQatar + GCC
MLOps Services
Scoped smallEvaluated, monitored

MLOps Services for Doha teams.

We build mlops services for teams in Doha that need working software, not a slide deck for next quarter's steering committee. Qatar's LNG-funded economy rewards teams who can act on data quickly, and Doha operators tell us the same thing every quarter: less theatre, more delivery. Our approach is MLOps infrastructure that turns ad-hoc ML into repeatable, monitored delivery, wrapped in evaluation and monitoring so quality is a number your team owns, not a vibe check. By the time we hand over, the system is deployed on your cloud, monitored on your dashboards, and covered by tests your engineers can read. Our team ships from Dubai and delivers into Doha and the wider GCC, so timezone, language, and data-residency get handled up front. What sets our mlops services delivery apart is that the engineer who scopes the build is the same engineer who ships it and shows up at the go-live call. If the project has already stalled once, the shape of the first release was usually wrong — that's fixable in a week, not a quarter.

Buyers in Doha are done with pilots. What they want now is one production system, measured, running, and reducing a real cost line or lifting a real revenue line. That's the frame we work inside.

What you actually get.

Value

One way to ship models

A shared training and serving pipeline that every model team uses. No more three data scientists doing three different things with three different notebooks.

Value

Drift and cost, monitored

Every production model surfaces drift, quality, and cost in one dashboard. When something moves, you get paged before customers or finance notice.

Value

Retraining as a boring job

Scheduled retraining, hold-out validation, promotion gates, and rollback — a system your team can operate without heroics.

Value

Infrastructure as code

The whole MLOps stack lives in Terraform and CI/CD, so a new team can bring up a working ML environment in an afternoon rather than a quarter.

Where MLOps earns its keep.

Use case

ML platform bring-up

A working MLOps platform — training, registry, serving, monitoring — on your cloud, in weeks, sized to your team.

Use case

Legacy ML modernisation

Take existing models running out of notebooks or ad-hoc scripts and wrap them in a proper pipeline, without changing the model itself.

Use case

Model monitoring rollout

Add drift and quality monitoring to production models that shipped without it, so the team stops finding out about problems from customers.

Use case

Ministries and QatarEnergy-adjacent firms

For QatarEnergy contractors, QFC-registered firms, and ministries in Doha, we deliver MLOps inside existing vendor frameworks and procurement rules. Compliance is a delivery input, not a surprise at UAT.

What we actually use.

PythonMLflowKubeflowAirflowTerraformKubernetesAWS SageMakerGCP Vertex AI

Common questions.

Do we need a full MLOps platform?

Depends on how many models and how many teams. For one or two models built by one team, an over-engineered platform slows you down. Once you have five or more models in production or multiple teams touching ML, the platform investment starts paying back in weeks per release. We help you make that call honestly rather than pushing a platform you don't need yet.

MLflow or Kubeflow — which do you recommend?

MLflow is lighter and easier to adopt, and covers most mid-market needs. Kubeflow is heavier but pays back when you have serious Kubernetes muscle and lots of teams shipping in parallel. We usually start with MLflow plus Airflow or a managed service, then graduate to heavier tooling only when the scale demands it.

Can you work with our existing cloud setup?

Yes. We deploy MLOps on your AWS, GCP, or Azure account, integrate with your existing identity provider, VPC, and CI/CD, and match your team's preferred infrastructure-as-code tooling (Terraform, Pulumi, or CDK). We're not trying to move you to a new platform — we're improving what you have.

How long does an MLOps bring-up take?

A working platform for one or two models is usually four to six weeks. Scaling to multi-team, multi-tenant, with governance and cost allocation, takes three to four months. We ship in stages so every stage is useful on its own — no big-bang launch, no year-long platform project.

How do you handle model governance?

Model registry with lineage, approval gates for promotion, mandatory documentation for each production model, and monitoring evidence attached to every release. For regulated clients we integrate with your existing change-management and audit tooling so governance evidence is generated automatically rather than compiled by hand.

Can you upskill our team as you go?

Yes, and we recommend it. Every engagement includes documentation, pairing sessions, and a hand-over plan so the platform is operable by your team when we leave. If your team wants deeper training in specific tools or practices we run workshops as part of the engagement.

Do you deliver into ministries and QatarEnergy-adjacent firms in Doha?

Yes. Public-sector and energy-adjacent work in Doha typically requires sitting inside an existing vendor framework and delivering under strict procurement rules. That's the shape we default to. We don't take POC-only work in Qatar — it wastes everyone's time — so we scope for production from the start.

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Ready to build?

Start with the smallest useful version.

We'll scope the first release, define the eval set, and give you a build plan you can hand to any engineering team — ours or yours.