Building an ML Experimentation Platform for Easy Reproducibility | Treeverse

ABOUT THE TALK:
Quality ML at scale is only possible when we can reproduce a specific iteration of the ML experiment–and this is where data is key.

In this talk, you will learn how to use a data versioning engine to intuitively and easily version your ML experiments and reproduce any specific iteration of the experiment.

This talk will demo through a live code example:
-Creating a basic ML experimentation framework with lakeFS (on Jupyter notebook)
-Reproducing ML components from a specific iteration of an experiment
Building intuitive, zero-maintenance experiments infrastructure
-All with common data engineering stacks & open source tooling.

ABOUT THE SPEAKER:
Vino Duraisamy is a developer advocate at lakeFS, an open-source platform that delivers git-like experience to object store based data lakes.
She has previously worked at NetApp (on data management applications for NetApp data centers), on data teams of Nike and Apple, where she worked mainly on batch processing workloads as a data engineer, built custom NLP models as an ML engineer and even touched upon MLOps a bit for model deployments.

ABOUT DATA COUNCIL:
Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers.

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Видео Building an ML Experimentation Platform for Easy Reproducibility | Treeverse канала Data Council