---
title: "Argo Workflows | Skills We Assign For | Azendo"
description: "Argo Workflows for Kubernetes pipelines — container steps, resource control, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/argo-workflows/"
---

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

# Argo Workflows.

Argo Workflows is a Kubernetes-native workflow engine where each step runs as a container in a DAG. It is used for machine learning pipelines, data processing and any multi-step containerised job.

## Where Argo Workflows fits on a long engagement.

Running every step as a container means each can have its own image, dependencies and resource request. A pipeline with a Python preprocessing step, a GPU training step and a Go evaluation step is natural here, where an orchestrator assuming one runtime makes it awkward.

Per-step resource specification is the practical advantage in machine learning. Requesting a GPU only for the step that needs one, rather than for the whole pipeline, is the difference between paying for an accelerator during a data download and paying for it during training.

## What an assigned team does with Argo Workflows.

It assumes Kubernetes fluency. Debugging means reading pod logs and events, and a team without that experience finds failures opaque in a way a managed orchestrator would not be.

Whether that is the right trade depends on whether the cluster is already part of the estate, which is assessed at scoping alongside [devops as a service](https://azendo.co/services/cloud-and-devops/) capacity.

## What we use Argo Workflows for.

* Steps with different runtimes Each stage in its own image, rather than one environment covering everything.
* GPU requested only where needed Per-step resources, so accelerators are not held during data movement.
* Pipelines beside the workloads One cluster and one set of tooling for training and serving.

## How Argo Workflows capacity is assigned.

Kubernetes-native pipelines are assigned across AI and platform capacity, with cluster fluency treated as a prerequisite.

## 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/)
* [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/)

## Tell us what your roadmap needs Argo Workflows 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/)