Orateur
Luca Antiga
Description
The advent of new machine learning approaches to astrophysics and particle physics comes with the challenge
of identifying a set of tools that can support machine learning-driven workflows for data analysis in this domain.
The list of available tools is growing by the day, and it can be challenging to identify a good starter set of tools.
In this talk we will explore a few building blocks of a reproducible data pipeline for data versioning, model development,
experiment tracking and model serving, such as Hangar, PyTorch Lightning, MLFlow and RedisAI.
We will provide an introductory overview of the each of these tools and the respective advantages. Finally, we
will present how we are leveraging these in the context of the ESCAPE project.
