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 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.
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.
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