A/B and shadow deployment.
Shadow deployment runs a new model alongside the current one on real traffic without serving its predictions. A/B deployment serves a fraction of traffic from the new model and compares outcomes.
Where A/B and shadow deployment fits on a long engagement.
Shadow deployment is the safest way to validate a model on production traffic. The new model sees real inputs and its predictions are recorded and compared, but nothing it says reaches a user — so a model that behaves badly on live data is discovered without anyone being affected.
It answers questions offline evaluation cannot. Real traffic contains distributions, edge cases and malformed inputs that a curated test set does not, and latency under production load is only measurable under production load.
What an assigned team does with A/B and shadow deployment.
A/B deployment is the next step and needs the same discipline as any experiment: a predefined metric, a sample size, and a decision rule agreed before results are visible.
Holding that sequence is what makes the comparison trustworthy, and it is the standard applied under ai engineering services.
What we use A/B and shadow deployment for.
- Validation on real traffic with no risk Predictions recorded rather than served, so bad behaviour affects nobody.
- Latency measured under real load Production performance established before the model serves anyone.
- A/B comparison with a stated rule Metric and decision threshold agreed before the results are known.
How A/B and shadow deployment capacity is assigned.
Deployment strategy is assigned inside AI capacity, with shadow validation preferred before any model serves live predictions.
Tell us what your roadmap needs A/B and shadow deployment for.
A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.
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