pipelines

Why we included this project

Kubeflow Pipelines is the machine learning workflow service inside Kubeflow, the Kubernetes-native ML toolkit, and it is a common choice for teams that want their model workloads to run as repeatable, scheduled jobs rather than ad hoc notebook sessions. You build a pipeline with the Python SDK from reusable components, and the service compiles it and orchestrates the run on your cluster, sequencing steps, retrying failures, caching intermediate results, and tracking artifacts and run history. Data scientists can experiment and iterate without writing orchestration glue, while platform engineers get one place to manage versions and the backend. It deploys as part of the full Kubeflow platform or standalone, and since version 1.8 it works with any container runtime through the Emissary executor, so it slots into existing Kubernetes infrastructure without much ceremony. For shops already committed to Kubernetes, this is a mature way to get a shared, governed path from experiments to production runs.