There have been some exciting releases of Kedro projects recently.
We dropped Python 3.7 support.
We added the
%reload_kedroto enable users to specify a source for project configuration.
We added validation for the configuration file used to override run commands via the CLI.
We decoupled the configuration loader to enable you to use it independently of a Kedro class. We did this by moving the definition of the default environments (
local) from the config loader to the
We enhanced the documentation with a new top-level navigation to easily switch between Kedro, Kedro Viz, and Kedro-Datasets documentation, and a new search-as-you-type to improve the search experience.
Kedro 0.19.x is contains some breaking changes, so if you’re upgrading from 0.18.x you should take a look at the new migration guide in the documentation.
Alongside the Kedro framework release, the team also put out a Kedro-Viz 7.0.0 release (followed in January 2024 by Kedro-Viz 7.1.0) and a set of Kedro plugins releases (
kedro-airflow 0.8.0, and
📣 Coming soon! We plan to hold an online community update session this month to discuss the release in more detail. Look out for details in the coming weeks!
Here in the Kedro team at QuantumBlack, AI by McKinsey, we are pleased to welcome Elijah Ko.
We were also thrilled to hear that Claypot AI is joining Voltron Data, which means that Deepyaman Datta, a long-time contributor to Kedro and part of the TSC, will now represent Voltron Data in the committee. We published his first blog post “Building scalable data pipelines with Kedro and Ibis” on the Kedro blog earlier this month and hope to see many more to come!
Recently on the Kedro blog
Recently published on the Kedro blog:
We’re always looking for collaborators to write about their experiences using Kedro. Get in touch with us on our Slack workspace to tell us your story!
Learn Kedro with these videos
You may have seen our recent blog post announcing our video course to teach you, hands-on, how to get up and running with Kedro. It’s structured in five parts, and each part is divided in short videos of 3 to 8 minutes that cover a specific part of the Kedro learning path. It is based on the spaceflights tutorial, so you can use the documentation as supporting material while watching the course.
Last month, Juan Luis from QuantumBlack, AI by Mckinsey, gave a talk at PyData Milan called “Who needs ChatGPT? Rock solid AI pipelines with Hugging Face and Kedro”. A video of the session is online, and explains how to create a complex AI pipeline using Hugging Face transformers, turn it into a Kedro project that cleanly separates code from configuration and data, and deploy it to production so it starts delivering value.
That’s it for this edition!
Don’t forget that we also make regular updates on the Kedro LinkedIn channel, on our Mastodon (https://social.lfx.dev/@kedro) and through our popular Slack community. Keep an eye on the QuantumBlack LinkedIn feed too!
You can bookmark this blog or add our RSS feed to your favorite reader to stay in the loop and join us next month for another update from the Kedro team.