MLOps Services in Abu Dhabi, done the way it should be: scoped small, measured on real usage, and handed over with docs and runbooks your engineers can read.
In Abu Dhabi, we run mlops services projects for operators who care about outcomes over demos and evaluation over adjectives. Most briefs we see out of Abu Dhabi come from energy, government, and finance — the vertical shifts, but the shape of the problem does not. So our default is MLOps infrastructure that turns ad-hoc ML into repeatable, monitored delivery, measured and iterated before anything touches production traffic. 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. We deliver across United Arab Emirates and the GCC in English and Arabic, with a project lead who owns delivery end-to-end rather than a chain of handoffs. The reason clients bring us back for the second and third mlops services project is the handover: docs, evals, runbook, and a person who picks up the phone. If you're comparing agencies, ask us how we measure success before we quote — that's usually the fastest way to see who's serious.
The UAE capital's energy and sovereign-wealth base is competitive, and Abu Dhabi operators don't get credit for AI theatre. What ships and reduces cost — or lifts revenue — is what earns the next budget round, and that's what we optimise for.
A shared training and serving pipeline that every model team uses. No more three data scientists doing three different things with three different notebooks.
Every production model surfaces drift, quality, and cost in one dashboard. When something moves, you get paged before customers or finance notice.
Scheduled retraining, hold-out validation, promotion gates, and rollback — a system your team can operate without heroics.
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.
A working MLOps platform — training, registry, serving, monitoring — on your cloud, in weeks, sized to your team.
Take existing models running out of notebooks or ad-hoc scripts and wrap them in a proper pipeline, without changing the model itself.
Add drift and quality monitoring to production models that shipped without it, so the team stops finding out about problems from customers.
For entities inside ADGM and the Abu Dhabi government, we build MLOps that respects data-residency, vendor-review, and procurement rules from the SoW onward. The cloud region and audit trail get decided before the first line of code.
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 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.
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.
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.
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.
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.
Yes. We deliver into ADGM-registered entities and Abu Dhabi government departments on a regular basis, which usually means tighter data-residency and vendor-review controls. We plan for those constraints inside the SoW rather than trying to bolt them on right before go-live, so audits and reviews rarely become the bottleneck.
SM Stratagem builds computer vision development in Abu Dhabi, United Arab Emirates. Vision for real environments. Edge or cloud, your call. Book a scoping call.
SM Stratagem builds ai consulting in Abu Dhabi, United Arab Emirates. Strategy that ends in a build. Roadmaps you can budget. Vendor-agnostic advice.
SM Stratagem builds ai integration services in Abu Dhabi, United Arab Emirates. AI inside the systems you already run. CRM, ERP, help desk, product.
SM Stratagem builds mlops services in Kuwait City, Kuwait. ML delivery, repeatable. Training pipelines you own. Monitoring and drift built in.
SM Stratagem builds mlops services in Manama, Bahrain. ML delivery, repeatable. Training pipelines you own. Monitoring and drift built in. Book a scoping call.
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.