Αdvanced Data Management on Kubeflow

In this talk, we’re going to present advanced data management on Kubeflow and discuss why it is essential for an end-to-end ML workflow. Why the Data Scientist should care and why the Data Engineer and DevOps should plan carefully for it.

Takeaways:

• Learn why data management is critical for an end-to-end ML workflow

• Learn how you can have all your work, along with your data, packaged, versioned and available across every step of your ML workflow

• Learn how you can create a Kubeflow Pipeline which is trackable, reproducible, and thus auditable with complete data provenance, tracing the lineage of all intermediate results.

• Learn how you can run ML workflows that span hybrid and multi-cloud environments