The Elyra pipeline editor in JupyterLab provides users with the means to assemble pipelines from Jupyter notebooks, Python scripts, R scripts and pre-packaged code using a GUI, without the need to write code.

The Visual Pipeline Editor is probably the most frequently used Elyra feature. Based on feedback we’ve received from users via our community channels, we’ve improved the editor to make it more powerful and easier to use. This blog post highlights three major improvements you’ll find in version 3.12. Pipeline node defaults When building…

With the release of version 3.3, the Elyra open source community has delivered a major milestone on our roadmap, enabling users to create pipelines using custom components. In this blog post I’ll summarize what custom components are and how to use them. Version 3.6 includes extended support for Apache Airflow…

The Elyra open source project for JupyterLab aims to simplify common data science tasks. Its most popular feature is the Visual Pipeline Editor, which is used to create machine learning pipelines without the need for coding. You can run these pipelines in JupyterLab or on Kubeflow Pipelines or Apache Airflow.

R support, Extended CLI, and more — October 2021: This blog post refers to an older version of Elyra. Refer to the latest blog post for the latest developments. It’s only been a couple weeks since the Elyra open source community published Version 2.1, which introduced experimental support for Apache Airflow. Version 2.2 delivers a couple of…

In this blog post I’ll introduce a GUI-driven approach to creating Apache Airflow workflows that implement machine learning pipelines using Jupyter notebooks and Python scripts. October 2021: This blog post refers to an older version of Elyra. Refer to the latest blog post for the latest developments. …

Earlier this month JupyterLab 3.0 was released, providing a slew of new features. In this blog post I’ll recap a few highlights and how the Elyra 2.0 extensions take advantage of them. March 2021 update: Elyra version 2.1 was released, adding support for running pipelines on Apache Airflow. April 2021…

In my previous blog posts Creating notebook pipelines using Elyra and Kubeflow Pipelines and Running notebook pipelines in JupyterLab I’ve introduced Elyra and the Visual Pipeline Editor, which enables you to assemble machine learning pipelines from notebooks and Python scripts without the need for any coding. In this blog post…

In Creating notebook pipelines using Elyra and Kubeflow Pipelines I’ve introduced Elyra’s Notebook Pipelines visual editor and outlined how you can assemble a machine learning workflow pipeline from a set of notebooks and run it on Kubeflow Pipelines. With the release of Elyra v1.1 …