kubeflow/kale
Kubeflow’s superfood for Data Scientists
Kale helps data scientists transform their interactive Jupyter notebooks into reproducible and scalable machine learning workflows on Kubeflow. It takes your existing Jupyter notebooks, which can include data exploration and model training code, and converts them into structured Kubeflow Pipelines. The output is an automated, production-ready machine learning pipeline that can be easily run and managed, even without deep expertise in Kubernetes or workflow orchestration platforms.
674 stars. Actively maintained with 30 commits in the last 30 days.
Use this if you are a data scientist who wants to easily convert your Jupyter notebooks into robust, shareable, and scalable machine learning pipelines on Kubeflow without writing complex code.
Not ideal if you do not use Kubeflow or Jupyter notebooks, or if you prefer to define your machine learning workflows programmatically using SDKs.
Stars
674
Forks
152
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 12, 2026
Commits (30d)
30
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/mlops/kubeflow/kale"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Compare
Related tools
skypilot-org/skypilot
Run, manage, and scale AI workloads on any AI infrastructure. Use one system to access & manage...
dstackai/dstack
dstack is an open-source control plane for running development, training, and inference jobs on...
ray-project/kuberay
A toolkit to run Ray applications on Kubernetes
volcano-sh/volcano
A Cloud Native Batch System (Project under CNCF)
m3dev/gokart
Gokart solves reproducibility, task dependencies, constraints of good code, and ease of use for...