---
title: "Hugging Face | Skills We Assign For | Azendo"
description: "Hugging Face and Transformers — model access, licensing, pinning, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/hugging-face/"
---

[Skills](https://azendo.co/skills/) AI and machine learning 

# Hugging Face.

Hugging Face hosts models, datasets and the Transformers library that provides a consistent interface to thousands of pretrained models. It is the default distribution point for open machine learning.

## Where Hugging Face fits on a long engagement.

A consistent interface across architectures is what makes the library valuable. Swapping one model for another to compare them is a string change rather than a rewrite, which makes empirical model selection cheap enough that teams actually do it.

The Hub is open, which means quality and provenance vary widely. A model with impressive benchmark numbers may have been evaluated on data that overlaps its training set, and licences on the Hub range from permissive to restrictive. Both need checking rather than assuming.

## What an assigned team does with Hugging Face.

Pinning versions matters more than it does in most dependency management. Models on the Hub can be updated in place, so a deployment that pulls the latest can silently change behaviour between one release and the next.

Pinning revisions and mirroring artefacts internally is basic supply-chain hygiene for machine learning, and it sits within the standards held under [managed ai services](https://azendo.co/services/ai-engineering/).

## What we use Hugging Face for.

* Comparing models cheaply A consistent interface, so evaluating alternatives is a configuration change.
* Provenance and licence checked Model origin and terms verified before anything depends on it.
* Revisions pinned and mirrored Fixed versions held internally, so behaviour cannot change between deployments.

## How Hugging Face capacity is assigned.

Model sourcing is assigned inside AI capacity, with licence and provenance checks treated as part of selection rather than a later review.

## Roles we assign Hugging Face for

* [LLM Engineer AI engineering](https://azendo.co/services/ai-engineering/llm-engineer/)
* [NLP Engineer AI engineering](https://azendo.co/services/ai-engineering/nlp-engineer/)

## Service lines it sits in

* [AI engineering](https://azendo.co/services/ai-engineering/)

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

## Related in ai and machine learning

* [PyTorch — skill we assign for](https://azendo.co/skills/pytorch/)
* [TensorFlow — skill we assign for](https://azendo.co/skills/tensorflow/)
* [scikit-learn — skill we assign for](https://azendo.co/skills/scikit-learn/)
* [Pandas — skill we assign for](https://azendo.co/skills/pandas/)
* [NumPy — skill we assign for](https://azendo.co/skills/numpy/)
* [embeddings — skill we assign for](https://azendo.co/skills/embeddings/)
* [function calling — skill we assign for](https://azendo.co/skills/function-calling/)
* [vLLM — skill we assign for](https://azendo.co/skills/vllm/)

## Tell us what your roadmap needs Hugging Face 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/)