---
title: "TensorFlow | Skills We Assign For | Azendo"
description: "TensorFlow in established ML estates — serving, edge deployment and migration, and the Azendo roles assigned for it."
url: "https://azendo.co/skills/tensorflow/"
---

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

# TensorFlow.

TensorFlow is a machine learning framework with a mature production ecosystem — serving, mobile and browser runtimes, and pipeline tooling. It appears most often in estates where models have been in production for several years.

## Where TensorFlow fits on a long engagement.

TensorFlow work is disproportionately maintenance of things that already run. A model trained four years ago, a serving setup nobody has touched, and a version upgrade everyone has been deferring because the migration path is unclear. That is ordinary and it is where the value is.

Its deployment story remains a genuine strength. Serving at scale, running on mobile, and running in a browser are all better supported than in most alternatives, which is often why the estate is on TensorFlow and why moving off it is less attractive than it first appears.

## What an assigned team does with TensorFlow.

Maintaining models somebody else trained is the normal condition of an established ML estate, and it rewards familiarity more than brilliance. A version upgrade deferred for two years is a week of work for whoever knows the estate and a month for whoever does not.

Where new feature work sits on top of the existing models, that is a different discipline again, assigned under [AI engineering services](https://azendo.co/services/ai-engineering/) on the same agreement as the operational capacity.

## What we use TensorFlow for.

* Upgrading a pinned version Moving off an old release with a tested path, rather than deferring it for another year.
* Running models on device Deployment to mobile or edge where latency or privacy rules out a round trip.
* Retraining an ageing model Refreshing a model whose accuracy has drifted, with evaluation that proves the new one is better.

## How TensorFlow capacity is assigned.

Production model work is assigned under [MLOps engineering](https://azendo.co/services/mlops-engineering/), because the problem is usually operational rather than statistical.

## Roles we assign TensorFlow for

* [Computer Vision Engineer AI engineering](https://azendo.co/services/ai-engineering/computer-vision-engineer/)
* [Machine Learning Engineer MLOps engineering](https://azendo.co/services/mlops-engineering/machine-learning-engineer/)

## Service lines it sits in

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

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

## Related in ai and machine learning

* [vLLM — skill we assign for](https://azendo.co/skills/vllm/)
* [MLflow — skill we assign for](https://azendo.co/skills/mlflow/)
* [Kubeflow — skill we assign for](https://azendo.co/skills/kubeflow/)
* [OpenAI — skill we assign for](https://azendo.co/skills/openai/)
* [Anthropic — skill we assign for](https://azendo.co/skills/anthropic/)
* [Google Gemini — skill we assign for](https://azendo.co/skills/google-gemini/)
* [Meta Llama — skill we assign for](https://azendo.co/skills/meta-llama/)
* [Google and open-weight models — skill we assign for](https://azendo.co/skills/google-and-open-weight-models/)

## Tell us what your roadmap needs TensorFlow 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/)