STEP 01
WHAT WE GROUND
Train
Bring datasets, checkpoints, and budgets into one managed training path. Lineage stays attached from the first step.
→DEVELOPERS · INFRASTRUCTURE · THE RAILS
Infrastructure.
GPUs, training, serving, and registry — wired into the review and the release. Platform, ML, and governance teams share one deployment path.
GPU · TRAIN · SERVE · REGISTER
LAB LAUNCH
14d → 3d
Infrastructure, policy defaults, and deployment hooks come together fast enough for real pilot timelines.
ONE VIEW
Cost + reliability
GPU usage, model serving, and experiment history visible in one place. No tab-hopping required.
SLO
99.9%
Service target for the rollout tier. Reliability goals, rollback controls, and alert routes defined first.
HOW IT WORKS
Train. Register. Deploy.
The rails under everything. Each step keeps its proof and routes the next reviewer to a known position in the system.
STEP 02
WHAT WE GATE
Register
Review the model version, lineage, and approval state before promotion. Every transition is a signed event.
→STEP 03
WHAT WE SHIP
Deploy
Ship the approved model with rollback, traffic controls, and release gates attached. Monitoring stays wired.
OPERATING CAPABILITIES
What teams need to launch.
Compute, storage, deployment, and evidence controls stay wired together. Each component has its place in the loop.
COMPONENT · 01
GPU MANAGEMENT
Capacity, tracked.
GPU pools, usage attribution, and cost visibility in one place. Platform teams keep spend and queue priority in view.
STARTS FROM · Pools · quotas · spend
COMPONENT · 02
MODEL SERVING
Rollout, controlled.
Roll out models with versioning, rollback, and traffic controls. Promotion stays inside the governed path.
STARTS FROM · Versions · rollback
COMPONENT · 03
TRAINING PIPELINES
Reproducible runs.
Run training jobs with checkpoints, tracked configs, and lineage attached. The next reviewer picks the run back up.
STARTS FROM · Checkpoints · configs
COMPONENT · 04
FEATURE STORE
Consistency across.
Keep features consistent across training and inference. The same shape on both sides of the deployment.
STARTS FROM · Online · offline
COMPONENT · 05
EXPERIMENT TRACKING
Compare. Reproduce.
Compare runs, baselines, and candidate models without hunting through notebooks. The diff stays attached.
STARTS FROM · Runs · baselines
COMPONENT · 06
MODEL REGISTRY
Promote with proof.
Promote approved models through environments with audit trails attached. Every transition is a signed event.
STARTS FROM · Stages · audit · sign
STACK
From data to deployment.
Four layers. Each one handles its part of the path. The integration points stay explicit.
01
Model + data layer
Datasets, checkpoints, and lineage attached before training starts. The feature store keeps shape consistent.
↳ Feature store · artifact storage · lineage
02
Training + orchestration
Runs stay reproducible while platform teams control spend and queue priority. GPU scheduling, checkpointing.
↳ GPU scheduler · job runner · checkpoints
03
Serving + release
Approved builds move into serving with rollback, traffic controls, and release gates already attached.
↳ Registry · traffic split · gates
04
Signals + downstream
Reliability, cost, and release events reach operators in Control Center alerts and workflow webhooks.
↳ Telemetry · billing · webhooks
INFRASTRUCTURE
Bring the plan. We map the path.
The checkpoints from training to deployment, mapped before launch. Cost, reliability, rollback, and evidence already wired.
↳ STARTS WITH
A rollout plan, a model, and the team ready to move it into production this quarter.
↳ LEAVES WITH
GPUs scheduled, registry policies set, rollback wired, alerts routed — before traffic moves.