Shadow Deployment

Full definition

Shadow mode logs both models' outputs and compares them offline. It catches issues canary cannot — for example, the new model is faster but produces a 12% higher rate of unsafe outputs that a real A/B would have exposed customers to. Shadow mode is the standard de-risking step before any model with potentially harmful outputs (LLMs, fraud, safety-critical scoring) is canaried.

Why it matters

For models where bad predictions create legal, safety, or trust damage that a 1% canary still expresses, shadow deployment is the only ethical path to live validation. AIDOLS recommends shadow mode for every regulated-industry model before canary.

Example

A health insurer shadows a new claims-triage model for 30 days, discovers it under-flags rare-disease claims by 8%, and corrects training data before any user ever sees a prediction.

Next steps

• Get a free workflow map →We map one of your workflows and give you a written verdict: software, a process change or AI. No obligation.
• AI consulting cost guide →Hourly rates and project budgets by firm tier, from independents to the Big Four.

Related terms

• Canary Deployment
  A canary deployment routes a small slice of production traffic (e.g., 1–5%) to a new model version while the majority continues hitting the stable version, surfacing latency, error, and quality regressions before full rollout.
• A/B Testing for ML
  A/B testing for ML is the practice of randomly splitting live traffic between a control model and a candidate model to measure the candidate's causal impact on real business metrics — revenue, conversion, retention, defect rate.
• Model Evaluation
  Model evaluation is the systematic measurement of an AI model's performance, safety, and behavior across representative tasks — using fixed benchmarks, golden datasets, human ratings, and LLM-as-judge methods — both before and after deployment.
• MLOps
  MLOps is the discipline of operating machine-learning and AI systems reliably in production — covering data pipelines, model training, deployment, monitoring, drift detection, governance, and incident response — analogous to DevOps for traditional software.

More deployment & operations terms

• Batch Inference
  Batch inference runs a model over a large set of inputs offline on a schedule — typically nightly or hourly — optimized for throughput and cost-per-prediction rather than latency.
• Online Inference
  Online inference returns model predictions synchronously in response to a live user or system request, within an interactive latency budget.
• Model Monitoring
  Model monitoring is the continuous measurement of a deployed model's inputs, outputs, and performance signals — including data drift, prediction drift, label drift, latency, and ground-truth accuracy — to detect degradation before it harms business outcomes.