---
title: "Weights & Biases | Skills We Assign For | Azendo"
description: "Weights & Biases for experiment tracking — comparability, sweeps, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/weights-and-biases/"
---

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

# Weights & Biases.

Weights & Biases is an experiment tracking platform recording hyperparameters, metrics, code versions and artefacts for each training run, with comparison, sweeps for hyperparameter search and a model registry.

## Where Weights & Biases fits on a long engagement.

Experiment tracking solves a problem every machine learning team has and few admit to. Dozens of runs with varying parameters, results in notebooks and filenames, and no reliable way to answer which configuration produced the best model or whether it can be reproduced.

Automatic capture is what makes it work. Logging that depends on someone remembering fails exactly when the work gets interesting, which is when runs are most numerous. Capturing code version, environment and parameters without being asked is the difference between a tool that is used and one that is abandoned.

## What an assigned team does with Weights & Biases.

The value compounds across a team rather than for an individual. Shared experiment history means a new specialist can see what has already been tried and why it was abandoned, instead of repeating it.

That accumulated record is one of the clearer arguments for continuity on machine learning work, which is what a [dedicated development team](https://azendo.co/services/dedicated-development-team/) provides.

## What we use Weights & Biases for.

* Runs that can be compared honestly Parameters and metrics captured automatically, so the best configuration is identifiable.
* Hyperparameter search managed Sweeps coordinated rather than run by hand and recorded in a spreadsheet.
* History a new specialist can read What was tried and abandoned, so the same ground is not covered twice.

## How Weights & Biases capacity is assigned.

Experiment tracking is assigned inside AI capacity, with automatic capture preferred over any process depending on discipline.

## Roles we assign Weights & Biases for

* [ML Platform Engineer MLOps engineering](https://azendo.co/services/mlops-engineering/ml-platform-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/)
* [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/)
* [Feast — skill we assign for](https://azendo.co/skills/feast/)

## Tell us what your roadmap needs Weights & Biases 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/)