---
title: "TensorFlow Serving | Skills We Assign For | Azendo"
description: "TensorFlow Serving in production — version management, batching, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/tensorflow-serving/"
---

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

# TensorFlow Serving.

TensorFlow Serving is a production serving system for TensorFlow models, providing versioning, automatic loading of new versions, batching and both gRPC and REST interfaces.

## Where TensorFlow Serving fits on a long engagement.

It is mature and efficient at what it does. Automatic version discovery, where a new model directory is loaded and traffic transitions without a restart, makes model updates genuinely routine rather than a deployment event.

The SavedModel format bundles the graph and weights together, which removes a class of mismatch between code and artefact. The signature definition also makes the serving interface explicit rather than implied by whatever the client sends.

## What an assigned team does with TensorFlow Serving.

Being framework-specific is the trade. It serves TensorFlow well and nothing else, so an organisation with mixed frameworks operates more than one serving stack.

Whether that specialisation is worth the additional surface is an architectural judgement made at scoping, as described in [how an assignment runs](https://azendo.co/how-it-works/).

## What we use TensorFlow Serving for.

* Model updates without a restart New versions discovered and loaded, so updating is routine rather than an event.
* An explicit serving interface Signature definitions, so the contract is stated rather than inferred.
* Efficient batching built in Throughput handled by the server rather than implemented per deployment.

## How TensorFlow Serving capacity is assigned.

Serving infrastructure is assigned inside AI capacity, with framework-specific and general servers weighed against total operational surface.

## Roles we assign TensorFlow Serving for

* [Model Deployment Engineer MLOps engineering](https://azendo.co/services/mlops-engineering/model-deployment-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

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

## Tell us what your roadmap needs TensorFlow Serving 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/)