DevOps Built for Dubai's AI-First Engineering Teams
LLMOps pipelines, model serving infrastructure, GPU-aware Kubernetes, and AI application deployment — the DevOps layer that AI-native Dubai companies need.
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AI-native DevOps is what happens when you apply production engineering discipline to AI and LLM applications. For Dubai AI companies building production AI products, the infrastructure and deployment practices matter as much as the models themselves.
Contact us for a free AI DevOps assessment — we’ll review your current model deployment workflow and identify the highest-leverage improvements for your Dubai AI team.
Engagement Phases
AI DevOps Assessment
Audit your current AI/ML deployment workflow. Map model lifecycle: training, evaluation, registration, staging, and production. Identify gaps in reproducibility, versioning, monitoring, and infrastructure efficiency.
LLMOps Pipeline Design
Design the target LLMOps pipeline: model registry, evaluation gates, canary deployment for model versions, and rollback procedures. For RAG-based systems: document pipeline, embedding versioning, and retrieval quality monitoring.
Infrastructure Build
Build GPU-aware Kubernetes configuration, model serving infrastructure (vLLM, TorchServe, or Triton), autoscaling for inference traffic, and cost controls for GPU utilisation. MLflow or similar experiment tracking integration.
Monitoring & Handover
Implement model performance monitoring: latency, token throughput, output quality metrics (where measurable). Runbooks for model incidents. Training for your Dubai AI engineering team.
Deliverables
Before & After
| Metric | Before | After |
|---|---|---|
| Model Deployment Frequency | Manual deployments — model updates take days and require a data scientist | Automated pipeline — model promotion takes under 30 minutes |
| GPU Utilisation | GPU instances idle 70% of the time — paying for unused capacity in Dubai | Autoscaled inference — GPU resources allocated on demand, cost reduced 60%+ |
| Experiment Reproducibility | Can't reproduce last month's best model — no versioning or artifact tracking | Every experiment reproducible from MLflow registry with full lineage |
Tools We Use
Frequently Asked Questions
What is LLMOps and why do Dubai AI companies need it?
LLMOps is the practice of applying DevOps principles to Large Language Model applications — versioning, testing, deployment pipelines, and monitoring for AI workloads. Dubai companies building AI-native products face specific challenges: model evaluation is harder than software testing, GPU infrastructure is expensive and requires careful autoscaling, and model behaviour can degrade silently without proper monitoring. LLMOps addresses all of these.
Do you work with both open-source models and commercial LLM APIs?
Yes. We work with teams using both self-hosted open-source models (Llama, Mistral, Qwen) and commercial APIs (OpenAI, Anthropic, Google). The infrastructure and deployment patterns differ significantly — self-hosted models require GPU orchestration and model serving infrastructure; API-based applications require rate limiting, fallback routing, cost tracking, and prompt versioning. We design for both.
What cloud infrastructure do you recommend for AI workloads in Dubai?
For GPU inference in the Dubai and GCC region: AWS me-south-1 (Bahrain) has the widest GPU instance availability for the region. Azure UAE North has GPU capacity for teams with Azure commitments. For training workloads, us-east or eu-west regions typically offer better GPU availability and pricing, with model artefacts replicated back to UAE regions for inference. We design for your specific UAE data residency requirements.
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Schedule a free DevOps consultation. We can have an engineer profiled and introduced within 48 hours.
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