---
title: "Neptune.ai | Skills We Assign For | Azendo"
description: "Neptune.ai for ML metadata — scale, flexible structure, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/neptune-ai/"
---

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

# Neptune.ai.

Neptune.ai is a metadata store for machine learning, logging experiments, model versions, datasets and artefacts with a flexible structure and a focus on handling very large numbers of runs.

## Where Neptune.ai fits on a long engagement.

Neptune is built for volume. Teams running thousands of experiments — extensive hyperparameter search, many model variants, continuous retraining — find that platforms comfortable with hundreds become slow to query, and the metadata store stops being useful at exactly the scale it is most needed.

Its structure is deliberately flexible rather than prescriptive, which suits teams with unusual requirements and means the organising conventions have to come from the team rather than the tool.

## What an assigned team does with Neptune.ai.

Metadata is most valuable at scale and most neglected there. A team running thousands of experiments without consistent naming and tagging has a large searchable store of runs nobody can find anything in.

Agreeing those conventions early and holding them is a discipline rather than a configuration, held by the assigned specialists as part of [managed ai services](https://azendo.co/services/ai-engineering/).

## What we use Neptune.ai for.

* Thousands of runs still queryable A store that stays fast at the scale where metadata matters most.
* Structure suited to unusual workflows Flexible organisation where a prescriptive tool would not fit.
* Conventions agreed up front Naming and tagging held consistently, or scale makes the store unsearchable.

## How Neptune.ai capacity is assigned.

Metadata management is assigned inside AI capacity, with naming conventions agreed before volume makes them unenforceable.

## 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

* [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/)
* [DVC — skill we assign for](https://azendo.co/skills/dvc/)
* [Feast — skill we assign for](https://azendo.co/skills/feast/)
* [feature stores — skill we assign for](https://azendo.co/skills/feature-stores/)

## Tell us what your roadmap needs Neptune.ai 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/)