kale

Pipeline builder

Simplifies the deployment of Kubeflow Pipelines workflows by providing a graphical interface for Data Scientists to define and deploy pipelines directly from JupyterLab.

Kubeflow’s superfood for Data Scientists

GitHub

632 stars
19 watching
128 forks
Language: Python
last commit: almost 2 years ago
Linked from 1 awesome list

jupyter-notebookkubeflowkubeflow-pipelinesmachine-learning

Backlinks from these awesome lists:

Related projects:

Repository Description Stars
kubeflow/katib Automated machine learning on Kubernetes using a framework-agnostic approach 1,509
kubeflow/pipelines A tool for building and managing machine learning workflows on Kubernetes. 3,614
minyus/pipelinex A Python package to build and experiment with machine learning pipelines using Kedro, MLflow, and other tools 224
deploykf/deploykf Builds machine learning platforms on Kubernetes by combining popular tools and services 376
nikenano/kubeflow-github-action An action for automating deployments of Kubeflow pipelines on Google Cloud Platform 35
quintoandar/butterfree A Python library for building data pipelines to create and load features into a feature store using Apache Spark. 283
quickube/piper Automates creation of Kubernetes workflows based on Git branch changes 21
druths/xp A tool for creating flexible and self-documenting data science pipelines 56
johnsonc/lambdo A workflow engine for unifying feature engineering and machine learning operations in data analysis pipelines 1
ploomber/soopervisor Tools for exporting and managing workflow pipelines across multiple platforms. 45
sematic-ai/sematic An open-source platform for building and managing machine learning pipelines with Python 974
combust/mleap Enables deployment of machine learning data pipelines and algorithms to production 1,504
elyra-ai/elyra An AI-centric extension to JupyterLab Notebooks 1,854
konstructio/kubefirst Automates the setup of cloud-native GitOps platforms using popular tools like Kubernetes and others. 1,806
kirillseva/ruigi A tool for designing and managing data processing pipelines in R. 42