// CLOUD· Specialty

Infrastructure that treats model shipping like code shipping.

For teams standing up training or inference infrastructure who don't want to reinvent CI/CD for ML from scratch.

The problem

Why this keeps biting teams

AI/ML infrastructure gets built ad hoc: a GPU box someone SSH'd into, a model artifact passed around manually, no real pipeline from training to serving. It works until more than one person needs to touch it.

What we do

[01]

Provision and right-size GPU capacity for training and inference workloads.

[02]

Build CI/CD pipelines for models, not just application code: versioned, tested, reproducible.

[03]

Stand up model-serving infrastructure that scales with request volume, not guesswork.

[04]

Add observability for model performance and drift, not just infrastructure health.

Typical Outcomes

faster

time from trained model to served endpoint

tracked

every model version, reproducible

Deliverables

[01]

GPU cloud provisioning (on-demand or reserved) sized to workload

[02]

CI/CD pipelines for model training, versioning, and deployment

[03]

Model-serving infrastructure with autoscaling

[04]

Model observability: latency, drift, and performance monitoring

Cloud migration & modernization

[02] FinOps & cost governance

FinOps & cost governance

[03] Hybrid & bare-metal provisioning

Hybrid & bare-metal provisioning

[04] Multi-tenant Kubernetes (vCluster)

Multi-tenant Kubernetes (vCluster)

DELVEX.

Stop guessing.
Start shipping.

Thirty minutes with a senior engineer. No sales deck, no junior SDRs. Just a technical conversation about what's actually breaking or slowing you down.

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