Kedro is an open-source Python framework designed to streamline the creation of reproducible, maintainable, and scalable data science and machine learning pipelines. By emphasizing best practices in software engineering, Kedro makes it easier for data teams to collaborate and build modular pipelines that can be executed seamlessly in production environments.

Features of Kedro:

Modular Pipeline Design: Enables the creation of reusable and well-structured components for data workflows, promoting clean and maintainable code.
Reproducibility: Ensures that data science projects can be replicated with consistent results by using standardized pipelines and configurations.
Integration with Other Tools: Supports integration with popular tools like Apache Airflow and Docker, making it easier to orchestrate workflows and manage deployments.
Data Versioning: Offers built-in tools for managing and tracking different versions of data used throughout the pipeline.
Scalability: Designed to scale with large datasets and complex workflows, making it ideal for production-grade data science projects.
Collaborative Workflow: Facilitates collaboration between data scientists, engineers, and other stakeholders by providing a structured approach to pipeline development.

To check the version of Kedro: kedro --version

Disclaimer: Kedro is an open-source software distributed under the Apache 2.0 License. It is not affiliated with, endorsed by, or sponsored by any specific organization. Users are encouraged to review the documentation and ensure compliance with licensing terms when using Kedro in their projects.

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