---
title: "Hire A/B and Shadow Deployment Specialists, Managed | Azendo"
description: "Our A/B and shadow deployment specialists test new models on real traffic before anyone is exposed to them. Managed for you, with one monthly fee."
url: "https://azendo.co/skills/a-b-and-shadow-deployment/"
---

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3. A/B and shadow deployment

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

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 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 rigour 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 the engineers in our [dedicated MLOps team](https://azendo.co/services/dedicated-mlops-team/) 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](https://azendo.co/hire-a-dedicated-development-team-in-thailand/) without opening an offshore office of their own.

## Roles that use A/B and shadow deployment

* [Model Deployment Engineer MLOps engineering](https://azendo.co/services/dedicated-mlops-team/model-deployment-engineer/)

## Service areas that use A/B and shadow deployment

* [MLOps engineering](https://azendo.co/services/dedicated-mlops-team/)

Capacity is agreed across your whole team, with one monthly fee, not per skill.

## Related in mlops and model delivery

* [MLOps handoff, a skill in our teams](https://azendo.co/skills/mlops-handoff/)
* [ONNX, a skill in our teams](https://azendo.co/skills/onnx/)
* [TensorRT, a skill in our teams](https://azendo.co/skills/tensorrt/)
* [Model registry, a skill in our teams](https://azendo.co/skills/model-registry/)
* [Weights & Biases, a skill in our teams](https://azendo.co/skills/weights-and-biases/)
* [Comet, a skill in our teams](https://azendo.co/skills/comet/)
* [Neptune.ai, a skill in our teams](https://azendo.co/skills/neptune-ai/)
* [DVC, a skill in our teams](https://azendo.co/skills/dvc/)

## Tell us what you need A/B and shadow deployment for.

A service delivery manager will reply with the specialists we'd suggest, the monthly capacity and what the first month looks like.

[Browse all skills](https://azendo.co/skills/)