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ML Model Deployer

Train + ship + monitor classical + LLM models.

Weeks → days
Model deployment time
3–5×
Models in production
−80%
Silent model failures
5–8 months
Payback period
Ml
ML Model Deployer
DA · No. 88 of 117
What it does

ML Model Deployer in production

  • Trains classical and LLM models on versioned data
  • Packages models into containers with signed artefacts
  • Promotes models through staging with approval gates
  • Monitors drift, latency and accuracy in production
  • Manages per-store or per-line model fleets
  • Rolls back automatically on quality regressions

45-second film: the ML Model Deployer tile, an animated mockup and the KPI impact.

How it ships

Live in 6–9 weeks, inside your perimeter

Deployment options
On-premise (air-gapped)Private cloudManaged cloudHybrid
Integrates with
MLflowDatabricksAmazon SageMakerAzure Machine LearningKubernetesWeights & BiasesGitHub Actions
Technology stack
MLflowKServevLLMEvidently AIPyTorch
Compliance & security
GDPRDPDPSOC 2 Type IIISO 27001
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A 20-minute technical walkthrough, no slides. Ships in 6–9 weeks.

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