128 skills found · Page 1 of 5
kedro-org / KedroKedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
kedro-org / Kedro VizVisualise your Kedro data and machine-learning pipelines and track your experiments.
Galileo-Galilei / Kedro MlflowA kedro-plugin for integration of mlflow capabilities inside kedro projects (especially machine learning model versioning and packaging)
Minyus / PipelinexPipelineX: Python package to build ML pipelines for experimentation with Kedro, MLflow, and more
kedro-org / Kedro CommunityExamples of data science projects created with Kedro.
kedro-org / Kedro PluginsFirst-party plugins maintained by the Kedro team.
NameArtem / Deployml CourseРепозиторий для открытого курса «Промышленная эксплуатация моделей машинного обучения»
kedro-org / Awesome KedroPlugins, extensions, case studies, articles, and video tutorials for Kedro
kedro-org / Kedro StartersTemplates for your Kedro projects.
getindata / Kedro KubeflowKedro Plugin to support running workflows on Kubeflow Pipelines
Minyus / Python Packages For Pipeline WorkflowThis article compares open-source Python packages for pipeline/workflow development: Airflow, Luigi, Gokart, Metaflow, Kedro, PipelineX.
tamsanh / Kedro GreatThe easiest way to integrate Kedro and Great Expectations
tgoldenberg / Kedro Mlflow ExampleNo description available
takikadiri / Kedro BootA kedro plugin that streamlines the integration between Kedro projects and third-party applications, making it easier for you to develop end-to-end production-ready data science applications.
Galileo-Galilei / Kedro Mlflow TutorialA tutorial on how to use kedro-mlflow plugin (https://github.com/Galileo-Galilei/kedro-mlflow) to synchronize training and inference and serve kedro pipeline
nasa / ML Airport Taxi OutThe ML-airport-taxi-out software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for four distinct use cases: 1) unimpeded AMA taxi out, 2) unimpeded ramp taxi out, 3) impeded AMA taxi out, and 4) impeded ramp taxi out. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.
getindata / Kedro AzuremlKedro plugin to support running workflows on Microsoft Azure ML Pipelines
getindata / Kedro VertexaiKedro Plugin to support running workflows on GCP Vertex AI Pipelines
Galileo-Galilei / Kedro PanderaA kedro plugin to use pandera in your kedro projects
deepyaman / Kedro AcceleratorKedro-Accelerator speeds up pipelines by parallelizing I/O in the background.
