---
title: "CI/CD for models | Skills We Assign For | Azendo"
description: "CI/CD for machine learning — evaluation gates, rollback, and the Azendo roles assigned for it."
url: "https://azendo.co/skills/ci-cd-for-models/"
---

[Skills](https://azendo.co/skills/) MLOps and model delivery 

# CI/CD for models.

CI/CD for models extends continuous delivery to machine learning: automated training, evaluation against thresholds, registration, and deployment with the ability to roll back — with the model treated as a versioned artefact.

## Where CI/CD for models fits on a long engagement.

Model deployment differs from application deployment in one decisive way: correctness is statistical rather than binary. Tests do not pass or fail; a model scores, and the pipeline needs a threshold policy deciding whether that score is good enough to promote.

Comparison against the current production model is the gate that matters. A new model must beat what is already deployed on the metrics that count, evaluated on the same held-out data, or promoting it is a change with no evidence behind it.

## What an assigned team does with CI/CD for models.

Rollback needs to be as fast for models as for code. When a deployed model starts behaving badly, reverting to the previous version should be a single operation rather than a retraining exercise.

That means retaining previous model artefacts and keeping the deployment path reversible, which is part of the delivery standard agreed as a [committed monthly capacity](https://azendo.co/pricing/).

## What we use CI/CD for models for.

* Promotion gated on evaluation A threshold policy, so a model reaches production on evidence rather than on schedule.
* Compared against what is deployed The incumbent as the baseline, evaluated on the same held-out data.
* Rollback as one operation Previous artefacts retained, so reverting does not mean retraining.

## How CI/CD for models capacity is assigned.

Model delivery pipelines are assigned across AI and platform capacity, with rollback treated as a required capability rather than an aspiration.

## Roles we assign CI/CD for models for

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

## Service lines it sits in

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

Capacity is agreed as a committed monthly capacity across a discipline, not per skill.

## Related in mlops and model delivery

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

## Tell us what your roadmap needs CI/CD for models for.

A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.

[All skills we assign for](https://azendo.co/skills/)