A/B and shadow deployment specialists for your dedicated team.

Where A/B and shadow deployment fits in a long-term product team.

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, while none of them reach a user, so bad behaviour on live data is found safely.

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 your dedicated 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 our dedicated MLOps team specialists apply.

Common A/B and shadow deployment use cases.

• Validation on real traffic with no risk Predictions recorded and compared, with no user affected.
• 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.

Adding A/B and shadow deployment skills to your team.

Deployment strategy is assigned inside AI capacity, with shadow validation preferred before any model serves live predictions.

Every A/B and shadow deployment specialist we assign works remotely inside your tools as a dedicated member of your team, with the management on our side. It is how companies hire remote developers without opening an offshore office of their own.