Kedro has significantly improved as a tool for MLOps and has maintained its focus on modularity and engineering practices, which we liked from the start. One step that highlights its modularity is the introduction of the standalone kedro-datasets package, which decouples code from data. Kedro has added enhancements in its CLI, starter project templates and telemetry capabilities. Additionally, the recent release of a VS Code extension is a good boost to the developer experience.
In the past we've talked about the improving tooling for applying good engineering practices in data science projects. Kedro is another good addition in this space. It's a development workflow framework for data science projects that brings a standardized approach to building production-ready data and machine-learning pipelines. We like the focus on software engineering practices and good design with its emphasis on test-driven development, modularity, versioning and good hygiene practices such as keeping credentials out of the codebase.
