Kubeflow specialists for your dedicated team.

Where Kubeflow fits in a long-term product team.

The argument for Kubeflow is consistency. If the organisation already runs Kubernetes, putting ML workloads there means one platform to secure, monitor and operate.

The argument against is that it inherits Kubernetes complexity and adds its own. A team with three models and no platform engineer will spend more time operating Kubeflow than doing machine learning. It earns its place at a scale most organisations have not reached.

What your dedicated team does with Kubeflow.

Running ML on Kubernetes means inheriting two operational surfaces, and organisations reach for it earlier than they should. The question at scoping is whether the number of models justifies the platform, and often it doesn't yet.

When it is justified, the work belongs with platform specialists. Where it is not, the same outcome is usually available from a dedicated data science team with far less to operate.

Common Kubeflow use cases.

• ML on the platform you already run Training and serving inside the existing cluster, with the same access control and monitoring.
• Repeatable training pipelines Multi-step workflows defined once and re-run on a schedule or on new data.
• Deciding whether you need it A clear assessment against simpler alternatives before committing to the operating cost.

Adding Kubeflow skills to your team.

Platform work of this kind is assigned to a dedicated MLOps team, and we will say when a simpler stack would serve you better.

Kubeflow specialists join your team remotely as dedicated, full-time team members, selected, employed and managed by us in Chiang Mai and Bangkok. Companies that hire offshore developers through Azendo add the skill at the next monthly cycle.