---
title: "Comet | Skills We Assign For | Azendo"
description: "Comet for experiment management — tracking through to production monitoring, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/comet/"
---

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

# Comet.

Comet is an experiment management platform covering tracking, model production monitoring, dataset versioning and artefact management, with both cloud and self-hosted deployment.

## Where Comet fits on a long engagement.

Comet's distinguishing feature is spanning the whole lifecycle rather than stopping at training. Tracking experiments and monitoring production model behaviour in one platform means the comparison between how a model scored and how it performs is available rather than assembled.

That continuity matters because the two are so often disconnected. A model with excellent offline metrics performing poorly in production is the standard machine learning disappointment, and diagnosing it needs both sets of numbers in the same place.

## What an assigned team does with Comet.

Self-hosting is available, which decides the choice where training data or production inputs cannot go to a third-party service.

Making that decision on data-protection grounds rather than convenience is part of responsible scoping, and what it covers is set out in [how the monthly fee is built](https://azendo.co/pricing/).

## What we use Comet for.

* Offline scores compared to production behaviour Both in one place, which is what makes the usual disappointment diagnosable.
* Datasets versioned alongside runs A complete record of what each model was trained on.
* Self-hosted where data cannot leave Deployment inside the estate when inputs are sensitive.

## How Comet capacity is assigned.

Experiment platform selection is assigned inside AI capacity, with hosting decided on data-protection grounds.

## 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/)
* [Neptune.ai — skill we assign for](https://azendo.co/skills/neptune-ai/)
* [DVC — skill we assign for](https://azendo.co/skills/dvc/)
* [Feast — skill we assign for](https://azendo.co/skills/feast/)

## Tell us what your roadmap needs Comet 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/)