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Machine Learning with Kubeflow

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Article from ADMIN 91/2026

By Holger Reibold

Finding the best approach and location for developing and operating computationally intensive machine learning models can be tricky. We explain how you can use Kubeflow to develop, deploy, and manage portable and scalable machine learning applications.

Artificial intelligence (AI) and machine learning (ML) are today's hot topics, but creating a model is only half the battle: You also need an infrastructure in which to operate, train, and deploy the model. Kubeflow [1] was developed for all these tasks. At its core, it aims to simplify the provisioning of ML projects by standardizing the phases of model development.

The typical modeling process comprises a sequence of recurring steps, the first of which is to identify a business challenge. On this basis, you then design a process. The next step is to select pertinent data and prepare the data for training, which also specifically means selecting and adapting algorithms.

The final step, deployment, determines whether the data is transmitted to an API or is output in some other way. Additionally, any admin worth their salt will want to employ monitoring to make sure no excessive data fluctuations or modifications occur because of changes in business requirements.

Designed for Kubernetes

Data scientists will encounter a number of challenging tasks, but Kubeflow is a targeted tool that facilitates the entire analysis process. Thanks to the use of machine learning operations (MLOps), the entire process can be designed efficiently. The original developer, Google, created the Kubeflow Pipelines (KFP) platform to address this issue, which means the entire design process can be divided into a number of logical blocks.

The first block is the exploratory data analysis (EDA) sub-process, the second encompasses training, the third tuning, and so on. The Kubeflow library comprises a selection of popular data science protocols. Another benefit is that the blocks in the pipeline are portable, meaning you can deploy them in different projects.

The "Kube" in the name indicates that the MLOps platform is designed for

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