AI Infrastructure & MLOps

From proof-of-concept to production, reliably
I build the platform that gets models into production: reproducible environments, MLOps pipelines, observability and GPU cost control. I focus on infrastructure and operations, not model development.

At a glance

Who it's for

Data and AI teams, CTOs and platform leads with models or AI solutions stuck at proof-of-concept that need to be industrialized: repeatable deployment, reliability and costs under control.

What I do here

I build the infrastructure and MLOps processes around the models: environments, CI/CD for ML, serving, monitoring and GPU scaling. The data scientist stays on the model; I take it to production and keep it there.

Typical outcomes

Repeatable, versioned deployment, shorter model release times, observability on drift and performance, and predictable GPU spend.

Focus areas

AI Platform & Environments

Reproducible environments for training and inference, GPU and quota management, team isolation. The foundation data scientists work on without friction.

MLOps Pipelines

CI/CD for models: data and model versioning, automated deployment, rollback and promotion across environments. From notebook to production with a process, not by hand.

Observability & Cost Control

Monitoring of performance, drift and availability of AI services, with GPU spend control through scaling, spot capacity and rightsizing.

Technologies & tooling

Azure AI Platform

Azure Machine Learning Azure OpenAI Azure AI Services Databricks Azure Kubernetes Service

MLOps & Automation

MLflow Azure ML Pipelines GitHub Actions Azure DevOps Model Registry

Serving & Scaling

Managed Online Endpoints AKS / GPU nodes KEDA Autoscaling Azure Container Apps

Observability & Governance

Azure Monitor Model Monitoring Data & Model Versioning Cost Management RBAC

Delivered scenarios

From PoC to Production for a Data Team

Industrialization of a model stuck in the experimental phase: environments, deployment pipeline and monitoring.

Outcome: repeatable, reliable releases, with the team autonomous over the model lifecycle.

Multi-Team MLOps Platform

Shared platform with isolation, GPU quota and CI/CD for multiple data teams.

Outcome: shorter deployment times and clear governance over environments and costs.

GPU Cost Control

Review of scaling and scheduling of AI workloads with spot capacity and rightsizing.

Outcome: more predictable GPU spend with no impact on production model performance.

Frequently asked questions on AI & MLOps

Do you also develop the machine learning models?

No. I handle infrastructure, deployment and MLOps: environments, pipelines, serving, monitoring and cost. The data scientist develops the model, I take it and keep it in production reliably.

Do you also work with LLMs and Azure OpenAI?

Yes, on the platform and integration side: deployment, security, cost management and observability of solutions built on Azure OpenAI and AI services, not on model fine-tuning itself.

Where do we start if I only have proof-of-concepts?

With an assessment of what's needed to get them to production: gaps in environments, versioning, deployment and monitoring. Then we industrialize a pilot case, not everything at once.

How do you keep GPU costs under control?

With scaling matched to load, spot capacity where the workload allows, rightsizing and scheduling. The goal is predictable spend without sacrificing production reliability.

Do you have models stuck at proof-of-concept?

If you need to industrialize AI and take it to production reliably, we can start with a platform assessment.

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