DevOps for the AI Era

AI applications require a different infrastructure stack. LLMOps, model serving, GPU-aware pipelines, and experiment tracking — we build the DevOps foundation that AI teams need.

Duration: 4-12 weeks Team: 1 AI DevOps Architect + 1 MLOps Engineer
The Challenge

You might be experiencing...

Your data science team produces models that never make it to production — the gap between Jupyter and a production API is a 6-month engineering project.
You're serving an LLM in production but have no observability — you don't know latency, cost per request, or drift.
Your AI application's infrastructure costs are unpredictable — GPU instances running 24/7 for workloads that run for 2 hours per day.
You need to A/B test two model versions in production but have no infrastructure for traffic splitting between model versions.

AI-native DevOps bridges the gap between AI research and AI production. As UAE engineering teams build more AI-powered products, the infrastructure underneath them — model serving, LLMOps pipelines, GPU orchestration, and AI observability — requires specialist knowledge that traditional DevOps engineers don’t always have.

Contact us to discuss your AI infrastructure challenges — free 30-minute consultation with our AI DevOps team.

Our Approach

Engagement Phases

Week 1-2

AI Infrastructure Audit

Assess current AI/ML infrastructure: how models are trained, versioned, deployed, and monitored. Identify the gap between experiment and production. Map GPU resource utilisation and cost.

Weeks 3-6

MLOps Pipeline

Implement ML pipeline: data versioning (DVC), experiment tracking (MLflow or W&B), model registry, and automated retraining triggers. Configure reproducible training environments with container-based jobs.

Weeks 7-10

Model Serving Infrastructure

Deploy model serving: vLLM or TGI for LLMs, Triton Inference Server for classical ML. Configure GPU-aware Kubernetes scheduling. Implement A/B testing and canary model deployments.

Weeks 11-12

LLMOps & Observability

Implement LLM-specific observability: token cost tracking, latency percentiles, prompt/response logging (with PII redaction), and model drift detection. Configure alerts for degraded model quality.

What You Get

Deliverables

AI infrastructure architecture diagram
MLOps pipeline (training → evaluation → registry → production)
Model serving infrastructure (GPU-aware Kubernetes)
Experiment tracking setup (MLflow or Weights & Biases)
LLM observability dashboard (cost, latency, quality metrics)
A/B testing infrastructure for model versions
GPU resource optimisation (spot instances, auto-scaling)
Expected Outcomes

Before & After

MetricBeforeAfter
Model Time to Production3-6 months: manual handoff from data science to engineering1-2 weeks: automated pipeline from training to serving
GPU Cost24/7 GPU instances for batch workloads50-70% cost reduction via spot instances and auto-scaling
AI Production VisibilityNo observability — flying blind on model performanceFull visibility: cost, latency, quality, and drift alerts
Technology

Tools We Use

MLflow / Weights & Biases vLLM / TGI / Triton NVIDIA GPU Operator / KEDA DVC LangSmith / Phoenix
Common Questions

Frequently Asked Questions

What is LLMOps?

LLMOps (Large Language Model Operations) is the set of practices for deploying, monitoring, and maintaining LLM-based applications in production. It extends MLOps with LLM-specific concerns: prompt versioning and evaluation, token cost management, context window optimisation, RAG pipeline observability, and safety monitoring. As LLMs become a core part of UAE engineering products, LLMOps is becoming as essential as standard DevOps.

Do we need GPU servers on-premise or can we use cloud GPUs?

For most UAE companies, cloud GPUs (AWS p3/p4/g5, Azure NCsv3, GCP A100s) are the right answer — they offer flexibility, no capital expense, and spot pricing for training workloads. On-premise GPUs make sense when: you have very high and predictable GPU utilisation (> 60%), you have strict data sovereignty requirements that prevent cloud, or you're running at a scale where reserved GPU capacity is cost-effective. We model the economics for your specific workload before recommending.

How do we evaluate LLM quality in production?

LLM quality evaluation in production uses a combination of: automated metrics (BLEU, ROUGE, BERTScore for summarisation tasks; exact match for structured outputs), LLM-as-judge (using a reference model to score outputs), human feedback collection via thumbs up/down or rating interfaces, and A/B testing between model versions. We implement the right evaluation approach for your use case — there's no one-size-fits-all LLM metric.

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