Test a model migration in stages: compare the candidate with the incumbent offline, validate deployment and rollback in staging, mirror production inputs without returning candidate outputs to users, then expose a controlled canary cohort only after pre-set gates pass. Shadow traffic checks candidate behavior on real-world inputs; a canary checks what happens when the candidate actually serves requests. Keep the incumbent available until the new model clears your quality, service-health, and safety checks.
What shadow traffic and canaries tell you
A model change can affect more than its headline task score. It may change tone, formatting, structured-output validity, latency, cost, or tool-calling behavior, with consequences for systems that consume its responses. Test both the model’s task performance and the surrounding application contract.
| Method | What it tests | Candidate output reaches users? | Useful when | Main limitation |
|---|---|---|---|---|
| Offline evaluation | Candidate behavior on a fixed, repeatable dataset | No | Establishing a baseline and catching known regressions early | The dataset may not represent live traffic patterns. |
| Shadow or mirror traffic | Candidate behavior on copied live inputs | No; the incumbent remains user-facing | Testing production-like inputs while keeping responses on the incumbent | Extra compute, privacy handling, duplicate side effects, and shared-state interference can complicate results. |
| Canary | Candidate behavior while it serves a limited share of live requests | Yes, for the canary cohort | Progressively validating the actual response path and user outcomes | Some users receive candidate outputs; detection and rollback must be ready. |
| Blue/green | A prepared parallel deployment switched into service after validation | Depends on when traffic is switched | Keeping a replacement ready for a quick traffic switch | Requires parallel capacity and sufficiently consistent environments. |
| A/B test | Comparative outcomes across assigned traffic groups | Yes, by design | Measuring comparative outcomes with a sound experiment plan | Requires good assignment and enough observations; it does not replace safety gates. |
AWS describes shadow deployment as running the new model alongside the existing one, while Google SRE discusses canaries and traffic teeing as separate deployment techniques. See AWS Prescriptive Guidance and Google SRE’s canary release guidance.
Set the comparison and gates before changing traffic
Record the incumbent’s task-quality and operational baseline, then specify what would count as acceptable performance for the candidate. Choose measures that fit the model’s job: for example, classification quality for a classifier or a task-specific evaluation for a generative model. If ground-truth labels arrive late or are unavailable, identify that limitation and use proxy signals only as interim indicators, not as proof of final quality.
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- Task quality: Compare with the incumbent using suitable labels, reliable review, or task-specific evaluation. Account for uncertainty when samples are small.
- API and integration behavior: Check response correctness, schema and format validity, and whether downstream integrations succeed.
- Service health: Track latency percentiles, errors, throughput, and resource or cost impact.
- Application guardrails: Monitor safety and business outcomes relevant to the application, plus output-distribution or prediction-skew checks where useful.
Write down how each metric is computed, the threshold that triggers action, the observation period, the alert route, and who can halt rollout. There is no generally valid canary percentage, quality threshold, or observation window: choose them for request volume, risk, service capacity, and how quickly your team can detect harm. Google Cloud’s reliability guidance includes prediction correctness, latency, throughput, and API function among relevant checks; Microsoft and AWS also describe using quality or model-performance signals in rollout decisions. See Google Cloud’s AI and ML reliability perspective, Microsoft Foundry model migration guidance, and AWS Prescriptive Guidance.
Run offline and staging checks first
Compare on a repeatable test set
Build a versioned set of representative requests and edge cases. Run both incumbent and candidate on the same inputs; compare task metrics and inspect meaningful output differences. Validate response formats and downstream assumptions, not just model scores. Offline results help catch known regressions, but they cannot establish how the candidate will behave under live workload and production load.
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Exercise the deployed endpoint in staging
Smoke-test the endpoint with ordinary, malformed, and edge requests. Verify authentication, request and response contracts, capacity, logging, monitoring, and the rollback procedure. Include cases such as missing features where relevant. Google Cloud recommends testing typical and edge cases and exercising rollback in staging. See Google Cloud’s ML solution guidance.
Mirror production inputs without changing user responses
- Deploy the candidate beside the incumbent. Keep the incumbent on the live response path.
- Copy a bounded share of eligible live requests. Send copies to the candidate and discard its responses; the incumbent continues to answer users.
- Compare behavior and record service signals. Examine predictions and output distributions, candidate latency and errors, and task quality when labels or reliable review are available.
- Prevent duplicate effects. Ensure replayed requests cannot write data, charge users, send messages, or trigger other actions. Isolate caches and mutable state where possible.
- Check data handling before copying traffic. Review authorization, privacy, retention, and access controls for the production inputs you mirror.
Shadow results show how the candidate handled copied inputs; they do not show candidate-driven user outcomes because users still receive the incumbent’s responses. Mirroring can also add compute load, while shared caches or state can distort comparisons. Microsoft’s safe rollout guidance for online endpoints, AWS’s MLOps deployment checklist, and Google SRE’s canary release guidance cover related rollout and traffic-mirroring considerations.
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Canary the response path in controlled steps
- Start only after shadow gates pass. Route a small share of eligible live requests to the candidate so its output can reach that cohort.
- Compare the exposed cohort with a suitable baseline. Monitor task quality and user or business outcomes alongside operational signals such as latency and errors.
- Hold at each step for the pre-set observation gate. Increase exposure only if the agreed metrics remain within bounds and the sample is informative enough for the decision.
- Stop or reverse traffic if a gate fails. Use the agreed alert and decision owner; do not improvise thresholds after seeing results.
The right traffic share and time at each step depend on request volume, risk, capacity, and detection speed; no source establishes one universal percentage or duration. AWS SageMaker’s canary guidance describes traffic shifting with monitoring, and Google Cloud reliability guidance discusses checks before shifting traffic. See Amazon SageMaker AI’s canary traffic shifting guidance and Google Cloud’s AI and ML reliability perspective.
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Before exposure begins, decide who may stop the rollout, which alerts or threshold failures trigger reversal, and exactly how routing returns to the incumbent. Keep the prior deployment reachable while evidence accumulates, and exercise the rollback in staging rather than relying on a written plan alone. After full promotion, continue monitoring: some quality issues become visible only when labels arrive or later analysis is possible. Google’s productionization guidance and Amazon SageMaker’s deployment guardrails address production rollout and rollback considerations.
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Common migration mistakes to avoid
- Treating offline results as a production guarantee: a fixed dataset can miss live input patterns, load-related latency, and edge behavior.
- Calling shadow a user-impact test: the candidate output is discarded, so this phase cannot measure outcomes caused by serving that output.
- Ignoring the effects of mirroring: duplicated work can raise load, and shared caches or mutable state can contaminate comparisons.
- Starting a canary without a decision rule: traffic shifting without defined metrics, alerts, and rollback is exposure without a controlled gate.
- Copying sensitive traffic casually: assess authorization, privacy, retention, and access controls; this workflow does not replace jurisdiction-specific legal advice.
- Removing the incumbent too soon: keep a tested route back while delayed quality evidence is still accumulating.
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