Scottish-sounding management consultancy McKinsey donated Kedro to the Linux Foundation.
Built in 2019, McKinsey launched Kedro as an open-source software tool on GitHub for data scientists and engineers.
In form and function, this technology is a library of code that can be used to build data and machine learning pipelines.
Cor is the core
The name Kedro derives from the Greek word meaning center or core.
The Kedro community now has a user base drawn from some 200,000 monthly downloads and over 100 contributors.
Regarding organizations willing to adopt Kedro as a standard for data science code, to model air traffic patterns and Telkomsel (Indonesia’s largest wireless network provider) uses kedro as standard across its data science organization.
Kedro will serve on the Linux Foundation LF AI and data area, a specialized umbrella foundation created in 2018 to support and accelerate development and innovation in Artificial Intelligence (AI) and data by supporting and connecting open source technical projects.
“We are delighted to welcome the Kedro project to LF AI & Data. It addresses the many challenges that exist in building machine learning products today and is a fantastic addition to our portfolio of hosted technical projects. We look forward to working with the community to grow the project footprint and create new opportunities for collaboration with our members, hosted projects, and the broader open source community,” says Dr. Ibrahim Haddad, Executive Director of LF AI & Data.
Yetunde Dada, product manager on Kedro said that Kedro is now in the hands of the data science ecosystem – and that’s the only way it can grow at this point, i.e. s ‘It’s improved by the best people around the world.
So you think you’re the industry standard?
This is new ground for McKinsey, the company has always protected its intellectual property… so what qualifies Kedro de facto as “industry standard” as he claimed.
Ivan Danov, Kedro’s technical lead says it’s a framework that borrows concepts from software engineering best practices and brings them to the world of data science. It lays all the groundwork to move a project from idea to finished product, allowing developers and engineers to focus entirely on solving the business problem at hand.
Kedro was actively developed prior to being open source and will continue to be the foundation for all advanced analytics projects within McKinsey.
Scar tissue, share with the birds
“We have been building ML products for a long time and on this journey we have accumulated a fair amount of scar tissue and learned many important lessons. The insights and guardrails that exist in Kedro reflect that experience and are designed to help developers avoid common pitfalls and follow best practices,” shares Joel Schwarzmann, Product manager.
Now, its future can be driven by a much wider range of stakeholders, across different industries, geographies, and technologies, who bring different perspectives and can apply Kedro to many more use cases.
An extended team of “maintainers”, including McKinsey’s own developers, can contribute to the development of Kedro: write code, develop product strategies, track use cases, and vote on decisions that affect the project.
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