MLOps & Machine Learning Platform Engineering — Saudi Arabia

Building a model is the easy part; running it reliably in production is where most AI initiatives stall. MLOps — the engineering discipline that takes machine-learning and LLM systems from notebook to dependable service — is what Skyline delivers for Saudi enterprises that have outgrown one-off experiments. We set up the pipelines, infrastructure, and governance that let your data-science teams ship models repeatedly, monitor them in the wild, and retrain them before they quietly decay.

Our MLOps and LLMOps work covers automated training and deployment pipelines (CI/CD for ML), model registries and versioning, feature stores, vector databases for retrieval, scalable model serving and inference optimisation, and continuous monitoring for accuracy, latency, and drift. We provision the right compute — managed cloud GPU, in-Kingdom regions, or on-premise GPU clusters — and we treat AI security seriously: access control, secrets management, model and prompt-injection defences, and audit trails. For organisations subject to SDAIA's AI governance direction, we help operationalise responsible-AI controls so models are explainable, monitored, and accountable.

• Model deployment, serving, and inference optimisation on cloud or on-premise GPU.
• CI/CD pipelines, model registry, feature store, and experiment tracking (e.g. MLflow, Kubeflow).
• Monitoring for accuracy, drift, bias, latency, and cost, with automated retraining triggers.
• LLMOps: fine-tuning, evaluation, vector databases, and RAG operations at scale.
• In-Kingdom / private AI infrastructure with PDPL-aware data handling and AI security.

Whether you are scaling predictive models for maintenance and demand forecasting, or industrialising generative-AI assistants, Skyline builds the operational layer so AI becomes a maintained capability, not a fragile prototype. We assess your current maturity, design a pragmatic target architecture, and implement it incrementally — with documentation, training, and optional managed operations for the long run.