AI / ML Infrastructure
Emerging capabilityInfrastructure for serving and operating models in production — treated with the same engineering discipline as any other production system.
The problem
AI and ML infrastructure is a genuinely new area for most engineering teams, and it inherits every DevOps problem — plus GPU cost, model versioning and inference latency on top. We're building this capability deliberately, alongside teams working through it for the first time, rather than claiming finished expertise we don't have.
Who it’s for
- Product teams moving a model from a notebook into production for the first time
- Engineering teams adding inference infrastructure to an existing platform
- Companies that need GPU infrastructure managed with the same rigor as everything else
What we do
- Design model serving and inference infrastructure on top of existing platform work
- Configure GPU-aware autoscaling and cost controls
- Apply the same observability and security discipline used across other infrastructure
- Establish model deployment and rollback processes
- Advise on build-vs-managed-service tradeoffs honestly, including when not to build custom infrastructure
Technologies that may be involved
What the engagement looks like
Diagnose
Understand the model, expected load, and current deployment approach.
Design
Propose a serving and infrastructure architecture matched to real requirements.
Build
Implement infrastructure with the same IaC and security standards as elsewhere.
Enable
Document operating procedures and cost controls for the team.
Typical deliverables
- Model serving architecture and infrastructure as code
- GPU cost and utilization visibility
- Deployment and rollback process for models
- Operating documentation for the platform team
What you own after delivery
- The infrastructure code, in your own repository
- Documented architecture decisions and their tradeoffs
- Cost and utilization visibility into GPU spend
Next step