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AI Architecture › MLOps & Learning
MLOps & Learning

MLOps Lifecycle for Fusion Control

MLOps at Kronos is the disciplined pipeline that carries a model from L0 training to a validated, revertible artifact allowed to influence the two machines.

STRATEGY / SLOW ▲ ▼ MICROSECOND REAL-TIMEL7Ecosystem & Strategytelemetry ▲ control ▼open ▸L6Experience & Visualizationtelemetry ▲ control ▼open ▸L5Applications & Copilotstelemetry ▲ control ▼open ▸L4Orchestrationtelemetry ▲ control ▼open ▸L3Twin Modeling & AItelemetry ▲ control ▼open ▸L2Data Fabrictelemetry ▲ control ▼open ▸L1Control Planetelemetry ▲ control ▼open ▸L0Foundationtelemetry ▲ control ▼open ▸PHYSICAL S.M.A.R.T. GENERATOR PLANTBREEDER · HYPERION1R0 1.2 m · A 2.5 · 16.84 T · δ −0.30BURNER · TANDEM MIRROR2317 T throat · 26.49 T plug · fₙ 5.44% · DEC1 center stack + plasma · 2 high-field plug · 3 expander → direct converterCOLOR GRAMMAR strategy AI-workflow infra/data models reactor/DECLINE SEMANTICStelemetry (µs)controlKRONOS FUSION ENERGYAI-NATIVE S.M.A.R.T. GENERATORMASTER BLUEPRINTSHEET 01REV. 2026-08L0-L7 · 2 MACHINES
The AI-Native S.M.A.R.T. Generator Master Blueprint — eight layers (L0→L7), one control stack, wired to both machines. Telemetry rises in microseconds; control descends the same path.

Why fusion needs hard MLOps

A model that mispredicts a plasma equilibrium or a plug-density trajectory is not a bad recommendation, it is a candidate machine fault. Kronos treats every model that can touch the breeder (Hyperion) or the burner (Aegis / MetroVolt) as a safety-relevant artifact with a full lifecycle: sourced data, versioned training, offline validation, staged deployment, monitoring, and a guaranteed rollback. Nothing reaches an actuator by accident.

The lifecycle mirrors the eight-layer S.M.A.R.T. stack. Training and heavy simulation live on L0; the resulting artifacts are compiled toward the real-time layers only after passing validation gates. The control plane (L1) never learns online; it executes only artifacts that MLOps has certified.

The seven stages

Both machines, one pipeline

The breeder needs equilibrium, shape, and disruption-avoidance models; the burner needs ambipolar-potential and end-plug models. The physics differs but the governance is identical. This uniformity is deliberate: one auditable pipeline, one registry, one rollback discipline, so that a reviewer certifying a burner controller and a reviewer certifying a breeder surrogate follow the same checklist.

Because the machines are still design and simulation studies with breeder construction beginning Q2 2027 and first-of-a-kind first tritium targeted near 2030, most of this pipeline runs today against the digital twin and L0 simulations, not hardware. That is the point: the MLOps discipline is built and exercised before any model is ever allowed near a coil supply.

Content reviewed August 2026 · design-and-simulation stage