Services
Machine Learning & MLOps Services

Machine Learning & MLOps Services in Dubai

Turn experimental models into reliable, revenue-driving ML products. Stralya designs, builds and operates robust MLOps foundations so your machine learning runs safely at scale in the cloud.

Service scope

What Our Machine Learning & MLOps Service Includes

Our Machine Learning & MLOps offering is designed for organisations that treat ML as a strategic asset, not an experiment. We cover the full lifecycle – from data and architecture to deployment and long-term operations – with a clear, structured scope adapted to Dubai and GCC requirements.

Core components of the ML & MLOps service

Assessment of your current ML stack, data landscape, tools and team capabilities.

Design of cloud-native ML and MLOps architecture on AWS, Azure or GCP.

Implementation of reproducible environments using containers and infrastructure as code.

Creation or hardening of data pipelines, feature stores and model registries.

Setup of CI/CD pipelines for ML, including automated testing and quality gates.

Design and deployment of real-time APIs, batch jobs or streaming ML workloads.

Implementation of monitoring, logging, alerting and model performance dashboards.

Drift detection, retraining workflows and governance for model lifecycle management.

Optional add-ons and extensions

Drift detection, retraining workflows and governance for model lifecycle management.

Advanced experimentation platforms (feature flags, A/B testing, online evaluation).

Custom analytics and reporting dashboards for business stakeholders.

Staff augmentation of senior cloud, data and MLOps engineers.

24/7 or extended-hours production support for critical ML services.

Integration with existing corporate data platforms and BI tools.

Security reviews and hardening in collaboration with your InfoSec team.

Every engagement starts with a structured discovery and scoping phase. From there, we build a tailored, fixed-price roadmap that matches your priorities, risk appetite and internal capabilities – ensuring your machine learning becomes a dependable part of your digital infrastructure, not a fragile side project.

Outcomes You Can Expect from Our ML & MLOps Work

From experiments to measurable impact

Your ML initiatives move from isolated notebooks to production systems that directly support revenue, efficiency or customer experience – with clear KPIs and reporting.

Performance, scalability and cost control

Cloud-native architectures ensure your models scale with demand while keeping infrastructure and inference costs under control through right-sized, observable workloads.

Reduced operational risk

Monitoring, alerting, drift detection and clear incident processes dramatically reduce the risk of silent model failures, degraded user experiences or compliance issues.

Empowered internal teams

Your data scientists, engineers and product teams gain a shared, well-documented platform and workflow, enabling them to collaborate efficiently and ship ML features faster.

Strategic advantage in the GCC market

With reliable ML capabilities embedded into your web products, you differentiate in a competitive Dubai and GCC landscape through smarter, more personalised and more efficient digital experiences.

How we work

A Structured MLOps Process from Idea to Stable Production

Every organisation is at a different stage in its ML journey. Some have models in notebooks, others have legacy pipelines that are difficult to maintain. Our approach adapts to your maturity, but always follows a rigorous, cloud-native process designed to reduce risk and increase predictability.

We start with a clear picture of your current ML initiatives, data sources, infrastructure and business objectives. We review existing models, pipelines, tools and team workflows, then map gaps against best-practice MLOps standards for reliability, security and scalability.

We design a target MLOps architecture tailored to your cloud (AWS, Azure or GCP), data stack and security constraints. You receive a clear, fixed-price scope covering environments, CI/CD, model serving, monitoring and governance – with realistic timelines and explicit responsibilities.

We build the core building blocks: infrastructure as code, containerisation, ML pipelines, feature stores, model registries, automated testing, and deployment workflows. Everything is versioned, reproducible and aligned with your existing engineering practices.

We take your models from notebooks to production endpoints or batch jobs, with strong observability: performance dashboards, alerts, drift detection and logging. We collaborate with your team to tune performance, cost and reliability, and to define SLAs that match the criticality of each use case.

We document everything and upskill your team, so they can confidently operate and extend the platform. Stralya can then stay on as a long-term partner – maintaining, improving and scaling your ML capabilities as your business and data grow.

Find Commonly Asked Questions

MLOps (Machine Learning Operations) is the set of practices, tools and processes that turn ML models into reliable, maintainable production systems. In Dubai, where digital projects often have high visibility and tight timelines, MLOps is critical to avoid fragile, manual deployments that break under real-world usage. It ensures your models are versioned, tested, deployed, monitored and retrained in a controlled, auditable way – aligned with your cloud, security and compliance standards.

Yes. Many of our engagements are with teams that have strong data science capabilities but lack the engineering capacity to industrialise their work. Stralya focuses on the MLOps and cloud-native engineering layer: infrastructure, pipelines, deployment, observability and governance. Your data scientists keep focusing on modelling, while we ensure their models can run safely and efficiently in production.

We are cloud-native and work with all three major cloud providers: AWS, Azure and GCP. We leverage their managed ML and data services when they make sense, and combine them with containers, Kubernetes or serverless architectures depending on your requirements, cost constraints and existing stack.

Yes. Stralya is often asked to rescue ML initiatives that are stuck in proof-of-concept mode, or that fail frequently in production. We start with a structured audit of your code, pipelines, infrastructure and processes, then propose a stabilisation and remediation plan. Our goal is not only to fix what is broken, but to leave you with a robust MLOps foundation that prevents the same issues from reappearing.

Our primary model is fixed-price, project-based. After an initial discovery and scoping phase, we define a clear scope, deliverables, timelines and responsibilities, then commit to a fixed budget. For selected organisations, we can also provide senior MLOps and cloud engineers in staff augmentation mode, usually to reinforce an existing team on a strategic initiative.

Absolutely. Stralya is first and foremost a cloud-native web development company. We design ML APIs, batch jobs and streaming pipelines that integrate cleanly with your existing web platforms, microservices and data infrastructure. This ensures your ML features feel like a natural extension of your product, not a separate, fragile add-on.

Case Studies