I won’t lie — there’s no coincidence that I introduce Kubeflow right after writing that tools can be difficult to be deployed. Ladies and gentlemen, meet Kubeflow — one of the most popular and yet one of the most irritating tools I used in years (CMake, brother, I will never forget you).

Kubeflow is one of the hottest things in ML and MLOps area recently with around 30 actively developed repositories with almost 20,000 stars from GitHub users. Does “The Machine Learning Toolkit for Kubernetes” make you think that this could be and do literally everything? Well, then you got it quite right. Kubeflow has few key components:

• Pipelines using YAML template or SDK for creating them and GUI (Fig. 2) for visualizing pipeline executions as well as their results,
• Jupyter Notebook Server,
• Katib — hyperparameter optimization or neural architecture search,
• Artifact Store,
• Dashboard (Fig. 1) web app to manage all of that and much more.

#kubernetes