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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesTest a replacement model in stages: compare it with the incumbent on a repeatable offline set, validate the deployment in staging, mirror production inputs in shadow mode, then expose a controlled share of live requests through a canary. Shadow traffic helps assess candidate behavior without returning its output to users; a canary tests the candidate in the response path. Define quality and service-health gates, alerts, owners, and a working rollback route before either production stage.
What shadow traffic and a canary actually test
Model migrations can change more than task scores. A candidate may produce different tone, formatting, structured output, latency, cost, or tool-calling behavior. Any of those changes can break downstream assumptions even when a headline quality metric looks stable.
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| Method | What it tests | Candidate output exposure | Useful when | Main limitation |
|---|---|---|---|---|
| Offline evaluation | Candidate behavior on a fixed, repeatable dataset | None | Establishing a baseline and catching known regressions early | The dataset may miss live traffic patterns. |
| Shadow or mirror | Candidate behavior on copied live inputs; its responses are discarded | None | Getting production-like coverage while the incumbent continues serving users | Extra compute, privacy handling, duplicate side effects, or shared-state interference can distort results. |
| Canary | Candidate behavior while it serves a small share of real requests | Limited, intentional exposure | Validating the response path and user outcomes progressively | Some users receive candidate outputs, so detection and rollback must be ready. |
| Blue/green | A full parallel deployment switched into service after validation | Depends on switch timing | Keeping a prepared replacement available for a quick traffic switch | Requires parallel capacity and environment consistency. |
| A/B test | Comparative outcomes across assigned traffic groups | Yes, by design | Measuring comparative outcomes with an experiment plan | Requires sound assignment and enough observations; it does not replace safety gates. |
These approaches answer different questions: shadowing compares candidate behavior on real inputs, while a canary tests what happens when candidate responses reach users. AWS describes shadow deployment as a new model running alongside the existing model in its continuous deployment MLOps checklist; Google SRE discusses canaries and traffic teeing in its canary release guidance.
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Set decision gates before moving traffic
Record the incumbent’s baseline and decide in advance what counts as acceptable for the candidate. The right task metric depends on the job: use classification quality for a classifier, and a task-specific evaluation for a generative model. If labels or ground truth arrive late, document that delay and use proxy signals only as interim indicators—not as proof of final quality.
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Write down how each metric is computed and what action follows if it crosses a gate. A useful gate set can include:
- Task-specific quality compared with the incumbent, with uncertainty considered when the sample is small.
- API correctness, schema or format validity, and downstream integration success.
- Latency percentiles, error rate, throughput, and resource or cost impact.
- Application-specific safety or business guardrails.
- Output-distribution or prediction-skew checks that could flag changed behavior.
Google Cloud’s AI and ML reliability guidance identifies prediction correctness, latency, throughput, and API function among pre-shift checks. Microsoft and AWS also recommend using model-quality or performance signals in rollout decisions. Their guidance does not establish one numeric threshold or observation window that fits every workload. Choose gates and observation periods based on request volume, risk, service capacity, and how quickly the team can detect harm.
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Run offline and staging checks first
Compare both models on the same test set
Build a versioned set of representative requests and edge cases. Run the incumbent and candidate against the same inputs, then inspect meaningful output differences—not just aggregate scores. Check formats, structured fields, and any behavior that downstream services depend on. Offline evaluation can expose known regressions before users are involved, but it cannot guarantee performance under production load.
Google Cloud’s guidance on developing high-quality predictive ML solutions recommends testing typical and edge cases. Keep the test set repeatable so later candidate versions can be compared on consistent inputs.
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Exercise the deployment in staging
Smoke-test the endpoint with ordinary, malformed, and edge requests. Verify authentication, request and response contracts, capacity, logging, and monitoring. Include requests with missing features where relevant. Test the rollback procedure in staging rather than assuming it will work under pressure; Google Cloud includes rollback testing among its model-validation guidance.
Mirror production inputs in shadow mode
- Deploy beside the incumbent. Keep the current model on the user-facing path and make the candidate available to receive copied requests.
- Bound the mirrored traffic. Send a controlled share of live inputs to the candidate, accounting for added compute and capacity. The candidate response should be recorded for analysis, not returned to the user.
- Compare behavior and service signals. Review prediction or output distributions, latency, and errors. Assess task quality when labels or reliable human review are available.
- Isolate side effects and state. Ensure duplicate requests cannot write records, charge users, send messages, or trigger other external actions. Separate caches and mutable state where possible.
- Review data handling before copying requests. Check authorization, privacy, retention, and access controls for production inputs and candidate logs.
Azure Machine Learning’s safe rollout guidance for online endpoints describes mirroring traffic for candidate evaluation. Google SRE’s traffic-teeing discussion also makes clear why copied requests need careful handling: mirroring can add work and shared state can interfere with results.
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Shadow results are evidence about candidate behavior on copied inputs, not direct evidence of user impact. Since the incumbent still supplies the response, shadow mode cannot show how users would react to the candidate’s answer or capture candidate-driven business outcomes.
Canary the candidate on the response path
- Start only after shadow gates pass. Confirm the candidate meets the predeclared quality, correctness, and service-health criteria.
- Route a small share of eligible live requests to it. Those requests can receive candidate outputs, so this is intentional, limited user exposure—not another shadow test.
- Compare the exposed cohort with a suitable baseline. Watch task quality and operational signals, as well as application-specific safety or business outcomes.
- Advance in controlled steps. Increase exposure only after the agreed observation gate passes; pause or reverse traffic if a gate fails.
AWS’s SageMaker canary traffic-shifting guidance describes staged traffic movement with monitoring and rollback controls. Google Cloud’s reliability guidance also emphasizes checking service behavior as traffic shifts. Neither establishes a universally safe canary percentage or duration: a sensible step size depends on traffic volume, the consequences of errors, service capacity, and detection speed.
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Promote or roll back deliberately
Before the first production request reaches the candidate, name the person or role authorized to stop the rollout, define the alert or threshold failures that trigger a pause or rollback, and specify how traffic returns to the incumbent. Keep the incumbent deployment available while evidence accumulates. Exercise the route back before launch; a rollback plan that has not been tested may fail when time matters.
Promotion does not end evaluation. Some labels, quality signals, and downstream effects arrive only after a delay, so continue monitoring after the candidate serves all traffic. Google for Developers’ productionization guidance covers production monitoring and model operations.
Quick Recap
Common ways a migration test can mislead
- Offline success is treated as a production guarantee. Fixed data may not capture live request patterns, load, or unusual edge cases.
- Shadow results are presented as user outcomes. Users still receive incumbent responses during shadowing.
- Mirroring changes the system being measured. Extra compute, shared caches, mutable state, or duplicate side effects can skew results.
- Canary traffic moves without meaningful gates. Without agreed metrics, alerts, and rollback authority, staged exposure is not a controlled experiment.
- Rollback is unavailable or too slow. If the incumbent is not reachable or reversal has not been exercised, a detected regression can continue affecting users.
- Sensitive traffic is copied casually. Production request replay requires appropriate authorization, privacy, retention, and data-access controls; the cited technical guidance is not jurisdiction-specific legal advice.
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