---
title: "TorchServe | Skills We Assign For | Azendo"
description: "TorchServe for PyTorch serving — handlers, versioning, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/torchserve/"
---

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

# TorchServe.

TorchServe is a serving framework for PyTorch models, providing a REST and gRPC interface, multi-model serving, versioning and custom handlers for preprocessing and postprocessing.

## Where TorchServe fits on a long engagement.

Custom handlers are where the real work sits. The handler defines preprocessing, inference and postprocessing, and it is the natural place to guarantee that the transformations applied at serving match those applied at training — the single most common source of production model defects.

Model versioning with multiple versions served simultaneously supports gradual rollout properly. Traffic can move between versions and revert quickly, which is what makes deploying a new model a reversible decision.

## What an assigned team does with TorchServe.

Being PyTorch-specific is a limitation worth acknowledging. An organisation with models in several frameworks ends up operating several serving stacks, which is more operational surface than one general server.

Standardising on one serving layer is usually worth a small loss of framework-specific convenience, and that architectural call is made at scoping as part of [how the monthly fee is built](https://azendo.co/pricing/).

## What we use TorchServe for.

* Transformations matched to training Handlers that guarantee the serving path mirrors the training path.
* Versions served side by side Gradual rollout with fast reversion, making deployment a reversible decision.
* A standard PyTorch serving path One consistent deployment rather than bespoke services per model.

## How TorchServe capacity is assigned.

Serving framework selection is assigned inside AI capacity, weighing framework fit against operating several serving stacks.

## Roles we assign TorchServe 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

* [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/)
* [Feature engineering — skill we assign for](https://azendo.co/skills/feature-engineering/)
* [cross-validation — skill we assign for](https://azendo.co/skills/cross-validation/)

## Tell us what your roadmap needs TorchServe 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/)