Stand up the stack that makes AI operational.
AuraOne infrastructure is for teams that need deployment speed, cost control, and reliability without stitching cloud primitives together by hand.
Infrastructure, policy defaults, and deployment hooks come together fast enough for real pilot timelines.
GPU usage, model serving, and experiment history stay visible in one place.
Reliability goals, rollback controls, and alert routes are defined before production traffic moves.
Stack + integration visual
One stack from data to deployment signals.
See how the stack fits together, what each layer handles, and what it unlocks for the team running it.
Model + data layer
Teams keep datasets, checkpoints, and lineage attached before training starts.
Feature store, artifact storage, experiment lineage
Training + orchestration
Runs stay reproducible while platform teams control spend and queue priority.
GPU scheduler, job runner, checkpointing, hyperparameter tracking
Serving + release
Deployment moves from approved build to serving tier without leaving the governed path.
Model registry, traffic splitting, rollback, release gates
Signals + downstream systems
Reliability, cost, and release events reach the operators who need to act next.
Telemetry, billing, Control Center alerts, workflow webhooks
Operating capabilities
Everything the deployment team needs between training and launch.
Managed compute, storage, deployment, and evidence surfaces with the operating controls already wired in.
GPU Management
Allocate, monitor, and schedule GPU resources across training jobs with usage tracking and cost attribution built in.
Model Serving
Deploy models with versioning, rollback, traffic splitting, and the reliability controls operators expect.
Training Pipelines
Managed training workflows with checkpointing, hyperparameter tracking, and reproducible experiment configs.
Feature Store
Centralized feature management with versioning, lineage tracking, and consistent serving across training and inference.
Experiment Tracking
Compare runs, track metrics, and reproduce results with every experiment linked to its data, code, and configuration.
Model Registry
Version, tag, and promote models through environments with approval workflows and audit trails at every stage.
How it works
The path from training run to live deployment.
- Step 01Train
Bring datasets, checkpoints, and budgets into one managed training path.
- Step 02Register
Review the model version, lineage, and approval state before promotion.
- Step 03Deploy
Ship the approved model with rollback, traffic controls, and release gates attached.
- Step 04Monitor
Watch latency, throughput, drift, and cost once the model is live.
Concrete scenario
Launch a domain lab without turning infrastructure into a six-month detour.
This is the scenario teams care about most: they need training, evaluation, serving, and control hooks fast enough to support a real rollout window.
Spin up a regulated domain lab with GPU pools, registry policies, and rollout targets already defined.
Run evaluation infrastructure beside training so drift, cost, and release readiness stay visible together.
Promote the approved model into serving with rollback, alerting, and cost attribution already wired.
Infrastructure work starts with cloud primitives, custom scripts, and weeks of rework before the first governed deployment exists.
Platform, ML, and governance teams share one deployment path with cost, reliability, rollback, and evidence controls already wired.