[joelschw]

Kedro sort of fits into a niche where it just overlaps somewhat with 'orchestrators' like Prefect, Metaflow, Dagster, Airflow and others. What makes it slightly different is that it it is focused on the rapid development journey to production, providing guardrails for teams to co-develop ML projects in a way that nudges software engineering best practice and clean code.

The 'finished article' in many cases should be deployed in production in one of those tools which provide specialised bells and whistles like scheduling, monitoring and observability.

Regarding MLFlow, there is also a slight overlap in terms of experimentation, but not things like model serving. Kedro has a mechanism to track experiments, but it's more designed to give users with zero infrastructure something for free out of the box. It's been built in a way that it can be repurposed for more dedicated experiment tracking tools - the folks at neptune.ai built their own plug-in for this purpose: https://docs.neptune.ai/integrations-and-supported-tools/aut...

[troiskaer]

Seems like Kedro has a similar thesis to Metaflow - I will look into it.

[vtuulos]

Yep, Kedro and Metaflow are more similar to each other than to other generic DAG orchestrators like Airflow.

Kedro and Metaflow make it easier to develop robust ML projects where orchestration plays an important role but it is not everything. They are two separate projects, so the way how they approach the problem differs greatly in details.