---
title: "Hire Cross-Validation Specialists for Honest Model Scores"
description: "Cross-validation specialists get grouping, time order and leakage right, so your model scores mean something. Dedicated data people, managed by us."
url: "https://azendo.co/skills/cross-validation/"
---

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3. Cross-validation

# Cross-validation specialists for your dedicated team.

Cross-validation estimates how a model will perform on unseen data by partitioning the training set into folds, training on some and evaluating on the rest, then averaging across the rotations.

## Where cross-validation fits in a long-term product team.

The standard errors are about splitting, not about the method. Random splits on grouped data, with several rows per customer, put the same entity in training and validation, which inflates the score because the model has already seen the answer. Grouped splitting is required whenever rows are not independent.

Preprocessing inside the validation loop is the other common leak. Scaling or imputing using statistics computed over the whole dataset leaks validation information into training, and the resulting estimate is optimistic in a way that only becomes visible in production.

## What your dedicated team does with cross-validation.

An optimistic validation estimate is worse than no estimate, because decisions get made on it. A model deployed on the strength of an inflated score consumes the credibility of the next project as well as its own.

Rigour here is a professional standard our specialists are held to through a [dedicated MLOps team](https://azendo.co/services/dedicated-mlops-team/).

## Common cross-validation use cases.

* Grouped splits where rows are related Entities kept whole across folds, so the score is not inflated.
* Preprocessing fitted inside the fold Statistics computed on training data only, preventing a subtle leak.
* Time order respected Forward-chaining validation on temporal data.

## Adding cross-validation skills to your team.

Model evaluation sits with the machine learning engineers we assign to you, with validation methodology part of the deliverable.

You can add cross-validation capacity as one dedicated specialist or as part of a wider remote team, with the people managed by us from our offices in Thailand. Companies [hire dedicated developers](https://azendo.co/hire-a-dedicated-development-team-in-thailand/) this way under one agreement and one monthly fee.

## Roles that use cross-validation

* [Machine Learning Engineer MLOps engineering](https://azendo.co/services/dedicated-mlops-team/machine-learning-engineer/)

## Service areas that use cross-validation

* [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 cross-validation 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/)