Overview
Most teams build great models and then struggle to ship them. MLOps and DevOps infrastructure is the gap between a working Jupyter notebook and a reliable production system. We provision GPU clusters, build CI/CD pipelines, and architect Kubernetes deployments — including air-gapped environments for classified workloads.
The Problem
ML teams spend more time managing infrastructure than building models. GPU costs spiral. Deployment takes weeks. Experiments are not tracked. Models that work in dev break in prod because the environment is not reproducible. DevOps teams that do not understand ML end up with ML pipelines that do not account for model drift, dataset versioning, or inference latency budgets.
Our Approach
We treat ML infrastructure like production infrastructure. Reproducible environments via Docker and Conda. MLflow for experiment tracking and model registry. Terraform and Ansible for infrastructure as code. Kubernetes (including air-gapped distributions) for orchestration. GPU cluster provisioning and decommissioning for training runs. Cost monitoring and FinOps tooling so you know what every training run costs.
Deliverables
- CI/CD pipeline design and build
- GPU cluster provisioning
- Air-gapped Kubernetes deployment
- MLflow model registry
- Terraform & Ansible IaC
- Cost monitoring & FinOps