Kedro

What it is

Kedro is a data and development workflow framework that implements best practices for building, testing, and deploying machine learning pipelines. It streamlines the process of creating reproducible and maintainable data science projects.

Kedro stands out by providing a robust framework that emphasizes reproducibility and maintainability, making it ideal for teams focused on productionizing machine learning models.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Frequent breaking changes between versions

   Historical migrations from v0.15 to v0.16 required significant adjustments in project structure and configuration, impacting existing projects' stability.

2. 02

   Limited documentation for advanced use cases

   While basic tutorials are provided, detailed guides on integrating Kedro with complex data storage solutions or customizing the framework's behavior are sparse.

3. 03

   Performance overhead due to abstraction layers

   The high-level abstractions and modular design of Kedro can introduce performance overhead, particularly in I/O-bound operations within data pipelines.

What it does well

• Reproducible pipelines with a modular structure

• Support for version control and testing of data pipelines

• Integration with various data storage solutions

• Extensive documentation and community support

Pricing

Free tier

YesOpen source — free to use

Paid from

$0

Model

Flat rate

Enterprise plan

No

Pricing not independently confirmed — check the vendor’s page.